diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 2124afe..44cfebb 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -11,3 +11,9 @@ updates: directory: "/" schedule: interval: "weekly" + ignore: + # The v5→v7 bump silently broke coverage uploads ("Missing Head Commit" + # on PRs). Keep codecov-action pinned until a deliberate, verified + # migration — see the comment in .github/workflows/CI.yml. + - dependency-name: "codecov/codecov-action" + update-types: ["version-update:semver-major"] diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 852dc10..c3e707b 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -19,12 +19,13 @@ jobs: permissions: # needed to allow julia-actions/cache to proactively delete old caches that it has created actions: write contents: read + id-token: write # OIDC token for tokenless Codecov uploads (see codecov step) strategy: fail-fast: false matrix: version: - '1.10' - # - 'nightly' + - '1.12' os: - ubuntu-latest arch: @@ -39,8 +40,22 @@ jobs: - uses: julia-actions/julia-buildpkg@v1 - uses: julia-actions/julia-runtest@v1 - uses: julia-actions/julia-processcoverage@v1 - - uses: codecov/codecov-action@v7 + # Pinned to v5: the dependabot bump to v7 (2026-06-25) silently broke + # uploads — Codecov has no commit newer than 2026-06-15, which is what + # produces "Missing Head Commit" on PRs. v5 is the last version verified + # to upload from this workflow. Before re-bumping, migrate deliberately + # (e.g. OIDC: `use_oidc: true` + `id-token: write` permission) and + # confirm a commit appears on Codecov. + # Authentication uses OIDC (`use_oidc` + the job's `id-token: write` + # permission) because the CODECOV_TOKEN secret is not set in this repo + # ("Token length: 0" in CI) and tokenless uploads are rejected on + # protected branches. OIDC requires the Codecov GitHub App to be + # installed for the organization; if uploads fail with an OIDC error, + # either install the app or set the CODECOV_TOKEN secret and replace + # `use_oidc` with `token: ${{ secrets.CODECOV_TOKEN }}`. + - uses: codecov/codecov-action@v5 with: files: lcov.info - token: ${{ secrets.CODECOV_TOKEN }} - fail_ci_if_error: false + use_oidc: true + # Fail loudly: a silent upload failure hid this breakage for weeks. + fail_ci_if_error: true diff --git a/.gitignore b/.gitignore index c019973..4ea4773 100644 --- a/.gitignore +++ b/.gitignore @@ -55,3 +55,6 @@ plan/ *_cuts.json settings.json *.sh +*-backup +*.cuts.json.stale_stronggrid +*strong.json diff --git a/README.md b/README.md index 710b710..0fc285c 100644 --- a/README.md +++ b/README.md @@ -54,22 +54,25 @@ using DecisionRules, JuMP, DiffOpt, Flux using SCS # 1) Build per-stage subproblems (DiffOpt-enabled) and collect: -# subproblems, state_params_in, state_params_out, uncertainty_sampler, uncertainties_structure +# subproblems, state_params_in, state_params_out, uncertainty_samples # 2) Build the deterministic equivalent over the full horizon det = DiffOpt.diff_model(() -> DiffOpt.diff_optimizer(SCS.Optimizer)) -det, uncertainties_structure_det = DecisionRules.deterministic_equivalent!( +det, uncertainty_samples_det = DecisionRules.deterministic_equivalent!( det, subproblems, state_params_in, state_params_out, Float64.(initial_state), - uncertainties_structure, + uncertainty_samples, ) +# deterministic_equivalent! remaps state_params_in/state_params_out in place. +# Copy those arrays first if you also need the original stage-wise refs later. + # 3) Train a TS-DDR policy end-to-end -num_uncertainties = length(uncertainty_sampler()[1]) # number of uncertainty components per stage +num_uncertainties = length(uncertainty_samples[1]) # number of uncertainty components per stage policy = Chain( Dense(DecisionRules.policy_input_dim(num_uncertainties, length(initial_state)), 64, relu), Dense(64, length(initial_state)), @@ -79,9 +82,9 @@ DecisionRules.train_multistage( policy, initial_state, det, - state_in_det, - state_out_det, - uncertainty_sampler; + state_params_in, + state_params_out, + uncertainty_samples_det; num_batches=100, num_train_per_batch=32, optimizer=Flux.Adam(1e-3), @@ -97,7 +100,7 @@ Single shooting solves one optimization per stage and rolls forward using the re ```julia using DecisionRules, Flux -num_uncertainties = length(uncertainty_sampler()[1]) +num_uncertainties = length(uncertainty_samples[1]) policy = Chain( Dense(DecisionRules.policy_input_dim(num_uncertainties, length(initial_state)), 64, relu), Dense(64, length(initial_state)), @@ -109,7 +112,7 @@ DecisionRules.train_multistage( subproblems, state_params_in, state_params_out, - uncertainty_sampler; + uncertainty_samples; num_batches=100, num_train_per_batch=32, optimizer=Flux.Adam(1e-3), @@ -126,7 +129,7 @@ Multiple shooting partitions the horizon into windows of length `window_size`. E using DecisionRules, Flux, DiffOpt using SCS -num_uncertainties = length(uncertainty_sampler()[1]) +num_uncertainties = length(uncertainty_samples[1]) policy = Chain( Dense(DecisionRules.policy_input_dim(num_uncertainties, length(initial_state)), 64, relu), Dense(64, length(initial_state)), @@ -149,10 +152,7 @@ DecisionRules.train_multiple_shooting( policy, initial_state, windows, - state_params_in, - state_params_out, - uncertainty_sampler; - window_size=24, # e.g., 6, 24, ... + uncertainty_samples; num_batches=100, num_train_per_batch=32, optimizer=Flux.Adam(1e-3), @@ -168,7 +168,8 @@ The training loops record metrics through a per-sample `SampleLog` cache and a p ```julia using DecisionRules, Random -# Materialize a FIXED held-out evaluation set once, before training +# Materialize a FIXED held-out evaluation set once, before training. +# Use stage-wise subproblems and parameter refs, not DE-remapped refs. Random.seed!(1234) eval_scenarios = [DecisionRules.sample(uncertainty_samples) for _ in 1:8] @@ -178,7 +179,7 @@ rollout_eval = RolloutEvaluation( policy_state=:realized, ) -train_multistage(policy, initial_state, det, state_in_det, state_out_det, uncertainty_sampler; +train_multistage(policy, initial_state, det, state_params_in, state_params_out, uncertainty_samples_det; num_batches=100, record=(sample_log, iter, model) -> begin rollout_eval(iter, model) @@ -202,6 +203,48 @@ Each evaluation reports (a) the rollout objective **excluding** the target-slack Per-sample debugging hooks can be attached with `SampleLog(on_sample=(s, models, log) -> ...)`; the training loop calls the hook after each sample's solve with the live JuMP model(s). The previous `record_loss=(iter, model, loss, tag) -> ...` keyword keeps working as a deprecated adapter. +## Strict mode and reachable policies + +The standard TS-DDR target constraint uses slack: + +```math +x_t + \delta_t = \hat{x}_t, +\qquad +\text{objective} += C_\delta \|\delta_t\|. +``` + +Slack makes training robust to unreachable targets, but it also makes the dual +signal depend on the target-penalty calibration. Strict mode removes the slack: + +```math +x_t = \hat{x}_t. +``` + +The resulting dual is the clean shadow price of imposing the target. The price +of that cleaner signal is feasibility: every policy target must be reachable +from the state used to condition the policy. + +This is automatic in the hydro strict subproblem path because each stage is +solved sequentially and the policy receives the realized previous reservoir +state. It is also possible in regular deterministic equivalents when the target +trajectory is rolled out from the true initial state using a reachable policy: + +```math +\hat{x}_0 = x_0,\qquad +\hat{x}_t = \pi_\theta(w_t, \hat{x}_{t-1}),\qquad +\hat{x}_t \in R(\hat{x}_{t-1}, w_t). +``` + +By induction, all targets are feasible, and the strict equalities force the +realized trajectory to match that reachable path. See the hydro example and the +DecisionRulesExa.jl companion for the GPU strict regular-DE implementation. + +The policy helpers separate two architectural choices: + +- recurrent `layers` / `DR_ENCODER_LAYERS` process uncertainty history only; +- `combiner_layers` / `DR_HEAD_LAYERS` add a nonlinear feed-forward + state-to-target head without recurrence over the state input. + ## GPU acceleration with DecisionRulesExa.jl For large-scale problems where the inner NLP solve is the bottleneck (e.g., AC-OPF with hundreds of buses), [DecisionRulesExa.jl](https://github.com/LearningToOptimize/DecisionRulesExa.jl) provides a GPU-accelerated backend that replaces JuMP with [ExaModels.jl](https://github.com/exanauts/ExaModels.jl) and solves with [MadNLP.jl](https://github.com/MadNLP/MadNLP.jl) + CUDSS on GPU. @@ -222,6 +265,24 @@ Examples live in `examples/`. Run tests with: julia --project -e 'using Pkg; Pkg.test()' ``` +## Repository Map + +| Path | Purpose | +|---|---| +| `src/DecisionRules.jl` | Module entrypoint and exports | +| `src/dense_multilayer_nn.jl` | MLP helpers, state-conditioned recurrent policies, nonlinear target heads | +| `src/simulate_multistage.jl` | Stage-wise and deterministic-equivalent simulation logic | +| `src/multiple_shooting.jl` | Windowed multiple-shooting setup, simulation, and training | +| `src/utils.jl` | Target-parameter utilities, deficit construction, rollout evaluation | +| `src/integer_strategies.jl` | Strategies for extracting gradients from integer/mixed-integer models | +| `src/score_function.jl` | Score-function gradient correction for nonsmooth/integer problems | +| `src/parameter_duals.jl` | Dual/sensitivity helpers for parameterized JuMP models | +| `docs/src/` | Documenter.jl manual pages | +| `examples/inventory_control/` | Inventory-control example and dynamic-programming/SDDP comparisons | +| `examples/rocket_control/` | Rocket MPC/control example | +| `examples/Experimental/` | Research prototypes and robotics/control explorations | +| `test/runtests.jl` | Package test suite | + ## Citation If you use this package in academic work, please cite: diff --git a/docs/make.jl b/docs/make.jl index 490de9b..743cb9d 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -20,19 +20,27 @@ makedocs(; format=Documenter.HTML(; prettyurls=get(ENV, "CI", nothing) == "true", canonical="https://LearningToOptimize.github.io/DecisionRules.jl", + size_threshold=300 * 1024, ), pages=[ "Home" => "index.md", - "Algorithm" => "algorithm.md", - "Gradient Fallback" => "gradient_fallback.md", - "Uncertainty Sampling" => "sampling.md", - "GPU Acceleration" => "gpu_acceleration.md", - "Examples" => [ - "Hydropower Scheduling" => "examples/hydro.md", - "Rocket Control" => "examples/rocket.md", - "Stochastic Lot-Sizing (Integer Variables)" => "examples/inventory.md", + "Part I — Theory" => [ + "Multistage stochastic optimization" => "theory/multistage.md", + "The TS-DDR framework" => "algorithm.md", + "Stochastic dual dynamic programming" => "theory/sddp.md", + "Extensions: mixed gradients, critics, risk" => "theory/extensions.md", + ], + "Part II — Package Guide" => [ + "Getting started" => "guide/getting_started.md", + "Uncertainty sampling" => "sampling.md", + "Gradient fallback" => "gradient_fallback.md", + "GPU acceleration" => "gpu_acceleration.md", + "API reference" => "api.md", + ], + "Part III — Case Studies" => [ + "Rocket control" => "examples/rocket.md", + "Stochastic lot-sizing (integer variables)" => "examples/inventory.md", ], - "API Reference" => "api.md", ], ) diff --git a/docs/src/algorithm.md b/docs/src/algorithm.md index 36c3d24..551566d 100644 --- a/docs/src/algorithm.md +++ b/docs/src/algorithm.md @@ -1,18 +1,23 @@ -# Algorithm +# The TS-DDR framework ```@meta CurrentModule = DecisionRules ``` -This page summarizes the TS-DDR (Two-Stage Deep Decision Rules) training algorithm. -For the full derivation, see [arXiv:2405.14973](https://arxiv.org/abs/2405.14973). +This chapter develops the TS-DDR (Two-Stage Deep Decision Rules) training +algorithm — the core of DecisionRules.jl. For the full derivation, see +[arXiv:2405.14973](https://arxiv.org/abs/2405.14973); for the general +problem class and where decision rules sit among solution methods, see +[Multistage stochastic optimization](@ref). ## Problem setting -Consider a ``T``-stage stochastic control problem where at each stage ``t`` we observe -an uncertainty realization ``w_t`` and must choose an action ``u_t`` that satisfies -stage constraints ``(u_t, x_t) \in \mathcal{X}_t(x_{t-1}, w_t)``. The goal is to -minimize the expected total cost: +Consider the ``T``-stage stochastic control problem of the previous +chapter: at each stage ``t`` we observe an uncertainty realization ``w_t`` +and must choose an action ``u_t`` that satisfies +stage constraints ``(u_t, x_t) \in \mathcal{X}_t(x_{t-1}, w_t)``. Restricting +attention to a parametric policy class, the goal is to +minimize the expected total cost over the parameters: ```math \min_\theta \; \mathbb{E}_{w_{1:T}} \left[ \sum_{t=1}^{T} c_t(x_t, u_t) \right] @@ -27,9 +32,13 @@ Instead of mapping observations directly to actions, the policy outputs **target states**: ```math -\hat{x}_{1:T} = \pi_\theta(w_{1:T}) +\hat{x}_t = \pi_\theta(w_t, \hat{x}_{t-1}), \qquad \hat{x}_0 = x_0, ``` +evaluated stage-wise with state feedback: each target is conditioned on the +previous target state during deterministic-equivalent training (or on the +realized state ``x_{t-1}`` in closed-loop rollouts). + A projection subproblem enforces feasibility by solving: ```math @@ -105,110 +114,170 @@ for k = 1, ..., ⌈T/W⌉: pass realized end-state to window k+1 ``` -**Pros**: balances coupling (within windows) with tractability; parallelizable windows. -**Cons**: continuity gaps between windows require penalty tuning. - -## Mixed gradient: score-function (REINFORCE) correction +**Pros**: balances coupling (within windows) with tractability; cheaper inner +solves than a full-horizon deterministic equivalent. +**Cons**: windows are chained sequentially during rollout/training because each +window needs the previous realized end-state; cross-window coupling is weaker +than in the full deterministic equivalent. + +## Beyond the pure dual gradient + +The dual gradient above is exact for smooth subproblems and unbiased over +fresh samples. Two extensions handle the situations where that is not +enough — **discrete decisions**, where the dual is local to a fixed +integer assignment and a score-function (REINFORCE) correction restores +the missing signal, and **small sample budgets**, where a control-variate +critic reduces the estimator's variance without moving its optimum. Both +are developed, together with a risk-averse change-of-measure variant, in +[Extensions: mixed gradients, critics, and risk](@ref); the score-function +correction is exercised in the +[Stochastic Lot-Sizing with Fixed Ordering Costs](@ref) case study. -For problems with integer variables or non-smooth subproblems, the dual -gradient can be biased — it is local to a fixed integer assignment and cannot -see the effect of discrete switches (e.g., opening a setup variable). +## Penalty annealing -DecisionRules provides a **score-function (REINFORCE)** correction that mixes -the dual gradient with a model-free policy gradient estimated from stage-wise -rollouts under perturbed targets. +The target penalty ``\lambda`` is critical: too small and the optimizer ignores +targets (no gradient); too large and the problem becomes ill-conditioned. DecisionRules.jl +supports a **penalty annealing schedule** that ramps ``\lambda`` during training: -### How the score-function estimator works +``` +Phase 1 (warmup): λ × 0.1 — let the policy explore +Phase 2 (nominal): λ × 1.0 — standard training +Phase 3 (tighten): λ × 10.0 — sharpen target tracking +Phase 4 (lock): λ × 30.0 — final precision +``` -1. **Perturb**: add Gaussian noise to the policy targets: - ``\tilde{x}_t = \hat{x}_t(\theta) + \delta_t``, where - ``\delta_t \sim \mathcal{N}(0, \sigma^2 I)``. +This is the `default_annealed` schedule, activated with `penalty_schedule=:default_annealed`. -2. **Rollout**: solve the stage-wise subproblems with the perturbed targets to - obtain realized costs ``R_m`` for ``m = 1, \ldots, M`` rollouts. These - rollouts solve the models exactly as built (MIPs stay MIPs), so the costs - reflect true integer-feasible decisions. +## Strict mode: penalty-free gradient signal -3. **Advantage**: center the costs ``A_m = R_m - \bar{R}`` (mean baseline - reduces variance without changing the expected gradient). +The standard TS-DDR formulation uses a penalty ``C_\delta \|\delta_t\|`` to +penalize deviations from the policy's targets. While effective, the penalty +introduces a trade-off: the dual ``\lambda_t`` conflates the **economic shadow +price** with a **penalty-correction term**. At high penalty, the gradient +signal tells the policy "reduce ``\delta``" rather than "be economically +optimal." -4. **Surrogate loss**: the differentiable scalar whose gradient recovers the - REINFORCE estimate: +**Strict mode** eliminates this coupling entirely by replacing the slack +constraint ``x_t + \delta_t = \hat{x}_t`` with a **hard equality**: ```math -L_{\text{sf}}(\theta) -\;=\; -\frac{1}{M} \sum_{m=1}^{M} - A_m - \sum_{t=1}^{T} - \left\langle - \frac{\delta_{m,t}}{\sigma^2},\; - \hat{x}_{t+1}(\theta) - \right\rangle. +x_t = \hat{x}_t \quad :\lambda_t ``` -This is the standard score-function estimator for Gaussian perturbations. -The key identity is -``\nabla_\theta \log p(\delta_t \mid \theta) = \delta_t / \sigma^2`` -for a Gaussian centered at ``\hat{x}_t(\theta)``. +There are no deficit variables, no penalty term, and no penalty to tune. The +dual ``\lambda_t`` is the **pure shadow price** ``\partial Q_t / \partial +\hat{x}_t`` — the marginal value of changing the target, uncontaminated by +any regularization. -### Mixed gradient +### The condition: target reachability -The final training gradient combines both signals: +A hard equality has no slack to absorb an unreachable target, so strict mode +is well-posed under exactly one condition: **every target the policy emits +must be attainable from the state the system is in when the corresponding +stage is solved.** Formally, let ```math -\nabla L -\;=\; -\alpha\, \nabla L_{\text{dual}} -+ (1 - \alpha)\, \nabla L_{\text{sf}}, +R(x, w) \;=\; \bigl\{\, x' \;:\; \exists\, u \text{ with } + (u, x') \in \mathcal{X}(x, w) \,\bigr\} ``` -where ``\alpha \in [0, 1]`` is the `dual_weight`. - -There are two separate solve paths in the mixed-gradient training loop: - -- **Dual path**: controlled by `integer_strategy`, which determines how local - dual information is read from the deterministic equivalent - (e.g., [`FixedDiscreteIntegerStrategy`](@ref) solves the MIP, fixes integers, - re-solves the LP, and reads LP duals). -- **Score-function path**: controlled by [`ScoreFunctionConfig`](@ref), which - owns separate rollout subproblems. These are solved exactly as built, and - their realized costs define the Monte Carlo score-function term. - -### Scheduled ramp-in +denote the **one-stage reachable set** — the states attainable from ``x`` +under realization ``w`` by some admissible action. Strict mode requires +``\hat{x}_t \in R(x_{t-1}, w_t)`` at every stage, where ``x_{t-1}`` is the +*realized* state. + +A **feasibility-guaranteeing policy** enforces this by construction: it +computes (an inner approximation of) ``R`` from its input state and maps the +network output into that set, typically by scaling a sigmoid-bounded output +across the reachable interval. The bounds carry no gradient; the gradient path +is solely through the network output, exactly as in the standard TS-DDR +pipeline. Constructing ``R`` is problem-specific. It is cheap whenever the +dynamics are linear in the controls with box bounds — resource-balance +equations are the canonical case — and the hydropower case study works out a +complete instance, cascade interactions included, in +[One-stage reachable sets](@ref) and its `HydroReachablePolicy` +implementation. + +### Validity in every formulation, by induction + +Reachability of each target from the *policy's input state* is enough to make +strict mode well-posed in **all** training formulations — stage-wise +subproblems, the embedded deterministic equivalent, and the regular +deterministic equivalent alike. The argument is one induction, and the strict +equality itself is what carries it: suppose ``\hat{x}_0 = x_0`` (the known +initial state) and every policy call returns a target reachable from the state +it conditioned on, -A [`ScoreFunctionSchedule`](@ref) can ramp ``\alpha`` from 1 (pure dual) to -its final value over a warmup period. Let ``k`` be the current iteration and -``\rho_k = \operatorname{clip}((k - k_0) / r,\, 0,\, 1)``. The effective -score-function weight is ``\rho_k (1 - \alpha)``. - -This lets the DE dual gradient establish a good initial policy before -introducing the higher-variance REINFORCE signal. - -See the [Stochastic Lot-Sizing with Fixed Ordering Costs](@ref) example for a -complete worked example with integer variables and mixed gradients. - -## Penalty annealing - -The target penalty ``\lambda`` is critical: too small and the optimizer ignores -targets (no gradient); too large and the problem becomes ill-conditioned. DecisionRules.jl -supports a **penalty annealing schedule** that ramps ``\lambda`` during training: - -``` -Phase 1 (warmup): λ × 0.1 — let the policy explore -Phase 2 (nominal): λ × 1.0 — standard training -Phase 3 (tighten): λ × 10.0 — sharpen target tracking -Phase 4 (lock): λ × 30.0 — final precision +```math +\hat{x}_t = \pi_\theta(w_t, \hat{x}_{t-1}) \in R(\hat{x}_{t-1}, w_t). ``` -This is the `default_annealed` schedule, activated with `penalty_schedule=:default_annealed`. +1. Stage 1 is feasible: ``\hat{x}_1`` is reachable from the true initial + state ``x_0 = \hat{x}_0``. +2. If stages ``1, \ldots, t`` are feasible, their strict equalities force + ``x_s = \hat{x}_s`` for ``s \le t``. The state the policy conditioned on + when producing ``\hat{x}_{t+1}`` is therefore *identical* to the realized + state ``x_t``, so ``\hat{x}_{t+1} \in R(x_t, w_{t+1})`` and stage ``t+1`` + is feasible. + +The formulations differ only in *which symbol* plays the policy input. In +stage-wise rollouts the policy reads the realized state ``x_{t-1}`` directly; +in the embedded DE it reads the solver's state variables; in the regular DE it +reads its own previous target ``\hat{x}_{t-1}``. Under strict equalities these +are the same object — the induction shows previous target ``\equiv`` previous +realized state — so no formulation is a special case and none needs a separate +argument. In particular, the regular DE is *not* an exception requiring extra +structure: the strict equality **closes the loop as a consequence**, it does +not presuppose a closed loop. + +### Information pattern: closed-loop vs. open-loop + +Distinct from the well-posedness question is the **information pattern**: does +the policy read the *realized* state (closed-loop feedback) or its *own +previous target* (open-loop target generation)? This axis matters +independently of strict mode: + +- In **non-strict** training, slack lets the realized state deviate from the + target, so the two inputs genuinely differ. A regular DE trains the policy + on target feedback while the optimizer realizes something else — a + train/deploy mismatch that shows up at evaluation (below). +- Under **strict equalities** the distinction collapses: realized state and + target are identical at every stage, so target feedback and realized + feedback are the same function evaluation, and training-time DE solves and + deployment-time stage-wise rollouts traverse identical trajectories. + +Keeping the two axes separate is the point: *strict-mode validity* is about +reachability of targets; *closed- vs. open-loop* is about what information the +policy consumes. Strict mode does not require closed-loop evaluation — it +makes the question moot by forcing the two information patterns to coincide. + +### When to use strict mode + +Whenever it is applicable — a feasible initial state and a policy constructed +to emit only **one-stage reachable** targets — strict mode is the preferred +formulation. The only reason to fall back to the penalty formulation is +numerical: some solvers degrade when the additional hard equality constraints +are imposed (the equalities remove the slack that otherwise absorbs small +constraint violations during intermediate iterates). + +Everything else follows as a beneficial side effect rather than a selection +criterion: there is no penalty hyperparameter to tune, no annealing schedule, +and the dual ``\lambda_t`` is the exact shadow price of the target — the +gradient signal is uncontaminated by a regularization term. + +In the [Hydropower Scheduling](@ref) case study, strict mode with a +reachable-set policy achieves competitive simulation costs out of the box, +with no penalty schedule, no annealing, and no hyperparameter search. ## Evaluation semantics -A policy trained on the deterministic equivalent generates targets using **target-state -feedback** (each target depends on the previous *predicted* target, not the realized -state). Evaluating such a policy with **realized-state feedback** (deployment semantics) -tests a different closed-loop path and will generally report higher cost. +A policy trained on the (non-strict) deterministic equivalent generates targets +using **target-state feedback** (each target depends on the previous *predicted* +target, not the realized state). Evaluating such a policy with **realized-state +feedback** (deployment semantics) tests a different closed-loop path and will +generally report higher cost. Under strict equalities the two modes coincide — +realized states equal targets identically — so the choice below is material +only when slack is present. [`RolloutEvaluation`](@ref) supports both modes via the `policy_state` keyword: - `:target` — matches DE training semantics (fair in-sample comparator) diff --git a/docs/src/assets/hydro_cost_comparison.png b/docs/src/assets/hydro_cost_comparison.png deleted file mode 100644 index c806f5a..0000000 Binary files a/docs/src/assets/hydro_cost_comparison.png and /dev/null differ diff --git a/docs/src/assets/hydro_generation_comparison.png b/docs/src/assets/hydro_generation_comparison.png deleted file mode 100644 index b601e59..0000000 Binary files a/docs/src/assets/hydro_generation_comparison.png and /dev/null differ diff --git a/docs/src/assets/hydro_training_convergence.png b/docs/src/assets/hydro_training_convergence.png deleted file mode 100644 index 3fd5455..0000000 Binary files a/docs/src/assets/hydro_training_convergence.png and /dev/null differ diff --git a/docs/src/assets/hydro_violation_share.png b/docs/src/assets/hydro_violation_share.png deleted file mode 100644 index 5f1d863..0000000 Binary files a/docs/src/assets/hydro_violation_share.png and /dev/null differ diff --git a/docs/src/assets/hydro_volume_comparison.png b/docs/src/assets/hydro_volume_comparison.png deleted file mode 100644 index 4e0f863..0000000 Binary files a/docs/src/assets/hydro_volume_comparison.png and /dev/null differ diff --git a/docs/src/assets/inventory_integer_results.png b/docs/src/assets/inventory_integer_results.png index 0f26dd2..9b80599 100644 Binary files a/docs/src/assets/inventory_integer_results.png and b/docs/src/assets/inventory_integer_results.png differ diff --git a/docs/src/assets/inventory_relaxed_results.png b/docs/src/assets/inventory_relaxed_results.png index 8a16f85..0556bfa 100644 Binary files a/docs/src/assets/inventory_relaxed_results.png and b/docs/src/assets/inventory_relaxed_results.png differ diff --git a/docs/src/examples/hydro.jl b/docs/src/examples/hydro.jl deleted file mode 100644 index e7bf3fd..0000000 --- a/docs/src/examples/hydro.jl +++ /dev/null @@ -1,481 +0,0 @@ -# # Hydropower Scheduling -# -# This example trains target-setting decision rules for the Bolivia -# long-term hydrothermal dispatch (LTHD) problem — both **TS-DDR** (deep, -# LSTM-based) and **TS-LDR** (linear) — and compares them against an SDDP -# baseline with inconsistent formulations. -# -# The Bolivia system has **10 hydro plants**, **96 monthly stages**, and -# **AC power flow** constraints. Inflow uncertainty is sampled from 47 -# historical scenarios. -# -# ## Overview of the TS-DDR approach -# -# Classical stochastic programming (e.g., SDDP) constructs piecewise-linear -# value-function approximations. TS-DDR takes a different route: a neural -# network policy ``\pi_\theta`` maps observations to **target states**, and a -# projection subproblem at each stage enforces physical feasibility while -# tracking those targets as closely as possible. -# -# The key insight is that the gradient of the projection subproblem with -# respect to the target parameters is available through Lagrange duality -# (or equivalently, implicit differentiation of the KKT conditions). -# This avoids differentiating through the full optimization solver. -# -# ## Problem formulation -# -# At each stage ``t``, the operator observes inflows ``w_t`` and the current -# reservoir state ``x_{t-1}``. The policy predicts target volumes: -# -# ```math -# \hat{x}_t = \pi_\theta(w_{1:t},\, x_{t-1}). -# ``` -# -# A stage subproblem projects onto the feasible set: -# -# ```math -# \begin{aligned} -# q_t(x_{t-1},\, w_t;\; \hat{x}_t) -# \;=\; -# \min_{x_t, u_t, \delta_t} -# \quad & -# c_t(x_t, u_t) + C_\delta\, \|\delta_t\| \\ -# \text{s.t.}\quad -# & x_t = x_{t-1} + w_t - \text{turbined}_t - \text{spilled}_t, -# && \text{(reservoir balance)} \\ -# & x_t + \delta_t = \hat{x}_t, -# && : \lambda_t \quad \text{(target constraint)} \\ -# & \text{AC-OPF}(u_t), -# && \text{(power flow)} \\ -# & x_t \in [0, \bar{x}],\; u_t \ge 0. -# \end{aligned} -# ``` -# -# The slack variable ``\delta_t`` absorbs infeasible targets; ``\lambda_t`` is -# the dual multiplier that provides the gradient signal. -# -# ## Gradient computation: the envelope theorem -# -# By the envelope theorem, the sensitivity of the optimal value with respect -# to the target parameter is simply the dual: -# -# ```math -# \frac{\partial q_t}{\partial \hat{x}_t} -# \;=\; -\lambda_t. -# ``` -# -# Combined with backpropagation through the policy network, the full gradient -# of the expected cost is: -# -# ```math -# \nabla_\theta \mathbb{E}[Q] -# \;\approx\; -# \frac{1}{S} \sum_{s=1}^{S} \sum_{t=1}^{T} -# \lambda_t^s \odot \nabla_\theta \hat{x}_t^s(\theta), -# ``` -# -# where ``S`` is the number of sampled trajectories per batch and ``\odot`` -# denotes elementwise multiplication. - -# ## Problem setup -# -# The JuMP subproblems are built from a MOF file (exported from PowerModels.jl) -# plus hydro data (reservoir limits, inflow scenarios). Each subproblem contains: -# - AC optimal power flow constraints -# - Reservoir balance: `vol_out = vol_in + inflow - turbined - spilled` -# - Target-slack deficit variables penalizing deviation from the policy's targets -# -# The helper `build_hydropowermodels` reads the case data, creates one JuMP model -# per stage, and parameterizes the initial volumes and inflows so they can be set -# at each training sample. - -using DecisionRules -using JuMP, DiffOpt, Ipopt -using Flux -using Statistics, Random - -# Load the problem builder (reads MOF + hydro JSON + inflow CSV). -# -# ```julia -# include("load_hydropowermodels.jl") -# ``` - -# ## Building the stage-wise subproblems -# -# Each subproblem is wrapped with `DiffOpt.diff_optimizer` so that Lagrange duals -# and implicit sensitivities are available for training. - -# ```julia -# diff_optimizer = () -> DiffOpt.diff_optimizer( -# optimizer_with_attributes(Ipopt.Optimizer, "print_level" => 0, "linear_solver" => "mumps") -# ) -# -# subproblems, state_params_in, state_params_out, uncertainty_samples, initial_state, max_volume = -# build_hydropowermodels( -# "bolivia", "ACPPowerModel.mof.json"; -# num_stages=96, -# optimizer=diff_optimizer, -# penalty_l1=:auto, penalty_l2=:auto, -# ) -# ``` - -# ## Policy architecture -# -# The policy is a [`StateConditionedPolicy`](@ref) with two components: -# -# 1. **Encoder** — a stack of LSTM cells that processes only the uncertainty -# (inflow) sequence, capturing temporal dependencies across stages. -# 2. **Combiner** — a Dense layer that merges the encoded uncertainty with the -# previous state to produce the next target. -# -# At each stage the policy receives ``[w_t;\; x_{t-1}]`` and outputs -# target reservoir volumes ``\hat{x}_t``: -# -# ``` -# ┌─────────┐ ┌────────────────┐ ┌──────────────┐ -# │ w_t │─────▶│ LSTM encoder │─────▶│ │ -# └─────────┘ └────────────────┘ │ Dense │──▶ x̂_t -# ┌─────────┐ │ combiner │ -# │ x_{t-1} │─────────────────────────────▶│ │ -# └─────────┘ └──────────────┘ -# ``` -# -# The LSTM carries hidden state across stages, giving the policy memory of -# past inflows. The activation is `sigmoid` (bounding outputs to ``[0,1]``, -# which is then scaled by the feasibility mapping). - -# ```julia -# models = state_conditioned_policy( -# num_uncertainties, num_hydro, num_hydro, [128, 128]; -# activation=sigmoid, encoder_type=Flux.LSTM, -# ) -# ``` - -# ## TS-LDR: Linear Decision Rules -# -# As a baseline, we also train a **linear** policy (TS-LDR). This uses -# `dense_multilayer_nn` with identity activation — a composition of linear -# layers equivalent to a single affine map: -# -# ```math -# \hat{x}_t = W [w_{1:t};\; x_{t-1}] + b. -# ``` -# -# TS-LDR uses the same target-setting framework and training pipeline as -# TS-DDR. The only difference is the policy class: linear maps have fewer -# parameters and cannot capture nonlinear inflow patterns, but they are a -# natural baseline from the classical LDR literature. - -# ```julia -# num_inputs = DecisionRules.policy_input_dim(num_uncertainties, num_hydro) -# models = dense_multilayer_nn(num_inputs, num_hydro, [64, 64]; activation=identity) -# ``` - -# ## Training pipeline 1: Deterministic Equivalent -# -# The deterministic equivalent (DE) couples all 96 stages into a **single NLP** -# for each sampled trajectory. This is the most direct formulation: the policy -# generates the full target trajectory ``\hat{x}_{1:T}`` in one forward pass, -# and a single coupled solve determines all realized states simultaneously. -# -# ### How it works -# -# ``` -# ┌──────────────────────────────────────────────────────────┐ -# │ For each sampled trajectory w_{1:T}: │ -# │ │ -# │ 1. Forward pass: x̂_{1:T} = π_θ(w_{1:T}, x_0) │ -# │ │ -# │ 2. Solve coupled NLP: │ -# │ min Σ_t c_t(x_t, u_t) + C_δ Σ_t ‖δ_t‖ │ -# │ s.t. dynamics + AC-OPF for ALL stages simultaneously │ -# │ x_t + δ_t = x̂_t(θ) ∀t (target constraint) │ -# │ │ -# │ 3. Read duals λ_t of target constraints │ -# │ Gradient: Σ_t λ_t ⊙ ∇_θ x̂_t(θ) │ -# └──────────────────────────────────────────────────────────┘ -# ``` -# -# ### Mathematical formulation -# -# ```math -# \begin{aligned} -# Q(w;\, \theta) -# \;=\; -# \min_{\{x_t, u_t, \delta_t\}_{t=1}^{T}} -# \quad & -# \sum_{t=1}^{T} c_t(x_t, u_t) -# + C_\delta \sum_{t=1}^{T} \|\delta_t\| \\ -# \text{s.t.}\quad -# & x_t = T_t(w_t,\, u_t,\, x_{t-1}), -# && t=1,\ldots,T \\ -# & x_t + \delta_t = \hat{x}_t(\theta), -# && : \lambda_t,\quad t=1,\ldots,T \\ -# & h_t(x_t, u_t) \ge 0, -# && t=1,\ldots,T -# \end{aligned} -# ``` -# -# The gradient is exact by the envelope theorem: -# -# ```math -# \nabla_\theta Q -# \;=\; -# \sum_{t=1}^{T} -# \lambda_t \odot \nabla_\theta \hat{x}_t(\theta). -# ``` -# -# **Advantages**: strongest gradient signal — full cross-stage coupling -# captures how a target at stage 3 affects costs at stage 50. -# -# **Disadvantage**: the NLP has ``96 \times (\text{AC-OPF variables})`` -# decision variables; the policy generates targets without seeing realized -# states (open-loop target generation). - -# ```julia -# det_equivalent, uncertainty_samples_det = DecisionRules.deterministic_equivalent!( -# det_model, subproblems_de, state_params_in, state_params_out, -# Float64.(initial_state), uncertainty_samples, -# ) -# -# train_multistage( -# models, initial_state, det_equivalent, -# state_params_in, state_params_out, uncertainty_samples; -# num_batches=4000, optimizer=Flux.Adam(), -# penalty_schedule=[(1,100,0.1), (101,210,1.0), (211,300,10.0), (301,4000,30.0)], -# ) -# ``` - -# ## Training pipeline 2: Stage-wise Decomposition (Single Shooting) -# -# Stage-wise decomposition solves one subproblem per stage sequentially. -# Unlike the DE, the policy operates in **closed loop**: after each stage -# solve, the realized state ``x_t`` (not the predicted target) is fed back -# as input to the next stage. -# -# ### How it works -# -# ``` -# ┌─────────────────────────────────────────────────────────────┐ -# │ For each sampled trajectory w_{1:T}: │ -# │ │ -# │ x_0 = initial state │ -# │ for t = 1, ..., T: │ -# │ x̂_t = π_θ(w_t, x_{t-1}) ← predict target │ -# │ solve stage-t subproblem ← project to feasible│ -# │ x_t = realized state from solver ← closed-loop │ -# │ accumulate c_t + C_δ ‖δ_t‖ │ -# │ │ -# │ Gradient: chain rule through all stage solves │ -# └─────────────────────────────────────────────────────────────┘ -# ``` -# -# ### Gradient chain -# -# The gradient must account for how the realized state at stage ``t`` -# depends on the targets at all earlier stages. By the chain rule: -# -# ```math -# \frac{\partial Q}{\partial \hat{x}_t} -# \;=\; -# \lambda_t -# + \sum_{k>t} -# \frac{\partial q_k}{\partial x_{k-1}} -# \cdot \prod_{j=t+1}^{k-1} -# \frac{\partial x_j}{\partial x_{j-1}} -# \cdot \frac{\partial x_t}{\partial \hat{x}_t}. -# ``` -# -# In practice, automatic differentiation (Zygote + ChainRules `rrule`s -# defined on each stage solve) handles this chain automatically. -# The `rrule` for each stage solve reads the dual ``\lambda_t`` for the -# target constraint and uses DiffOpt's implicit differentiation for the -# state-transition sensitivities. -# -# **Advantages**: closed-loop — the policy sees realized states, matching -# deployment semantics. Each solve is small (single-stage AC-OPF). -# -# **Disadvantage**: gradients weaken over long horizons because the -# chain rule multiplies many Jacobians; sequential solve prevents -# parallelism. - -# ```julia -# train_multistage( -# models, initial_state, subproblems, -# state_params_in, state_params_out, uncertainty_samples; -# num_batches=3000, optimizer=Flux.Adam(), -# penalty_schedule=:default_annealed, -# ) -# ``` - -# ## Training pipeline 3: Multiple Shooting -# -# Multiple shooting partitions the ``T``-stage horizon into ``K`` windows of -# ``W`` stages each. Within each window, a local deterministic equivalent -# couples the stages (strong gradient signal). Between windows, the realized -# end-state is passed to the next window (closed-loop continuity). -# -# ### How it works -# -# ``` -# ┌────────────────────────────────────────────────────────────────┐ -# │ Partition T=96 stages into K=⌈96/12⌉=8 windows of W=12 │ -# │ │ -# │ x_0 = initial state │ -# │ for k = 1, ..., K: │ -# │ stages = [(k-1)W+1, ..., kW] │ -# │ x̂_{stages} = π_θ(w_{stages}, x_{start_k}) │ -# │ solve window-k DE (12-stage coupled NLP) │ -# │ x_{end_k} = realized end-state from window solve │ -# │ x_{start_{k+1}} = x_{end_k} │ -# │ │ -# │ Gradient: │ -# │ Within window: duals from the coupled solve (like full DE) │ -# │ Across windows: DiffOpt chain rule through end-states │ -# └────────────────────────────────────────────────────────────────┘ -# ``` -# -# ### Gradient structure -# -# Let ``Q_k`` be the cost of window ``k``. The total cost is -# ``Q = \sum_k Q_k``. Within a window, the gradient is identical to the -# DE case (duals of the target constraints in the coupled model). Across -# windows, the chain rule threads through the realized end-state: -# -# ```math -# \frac{dQ}{d\theta} -# \;=\; -# \sum_{k=1}^{K} -# \left( -# \frac{\partial Q_k}{\partial \hat{x}_k} -# \cdot \frac{\partial \hat{x}_k}{\partial \theta} -# \;+\; -# \frac{\partial Q_k}{\partial x_{\text{start}_k}} -# \cdot \frac{d x_{\text{start}_k}}{d\theta} -# \right), -# ``` -# -# where ``\frac{d x_{\text{start}_k}}{d\theta}`` involves the chain -# through all prior windows via ``x_{\text{end}_{k-1}}``. -# -# **Advantages**: balances gradient quality (12-stage coupling) with -# tractability (8 small DEs instead of one large one); inter-window -# chain provides some closed-loop signal. -# -# **Disadvantage**: window boundaries introduce gradient discontinuities; -# the full-horizon coupling is weaker than the single DE. - -# ```julia -# windows = DecisionRules.setup_shooting_windows( -# subproblems, state_params_in, state_params_out, -# Float64.(initial_state), uncertainty_samples; -# window_size=12, -# model_factory=() -> DiffOpt.nonlinear_diff_model(ipopt_attrs), -# ) -# -# train_multiple_shooting( -# models, initial_state, windows, () -> uncertainty_samples; -# num_batches=3000, optimizer=Flux.Adam(), -# penalty_schedule=:default_annealed, -# ) -# ``` - -# ## Penalty annealing -# -# The target penalty ``C_\delta`` controls the trade-off between following -# the policy's targets and minimizing operational cost. DecisionRules -# supports a **penalty annealing schedule** that ramps the penalty multiplier -# during training: -# -# | Phase | Multiplier | Purpose | -# |:------|:----------:|:--------| -# | Warmup | ``0.1 \times C_\delta`` | Let the policy explore freely | -# | Nominal | ``1.0 \times C_\delta`` | Standard training | -# | Tighten | ``10.0 \times C_\delta`` | Sharpen target tracking | -# | Lock | ``30.0 \times C_\delta`` | Final precision | -# -# This is activated with `penalty_schedule=:default_annealed` or by passing -# an explicit list of `(start_iter, end_iter, multiplier)` tuples. - -# ## Evaluation -# -# After training, we evaluate the policy using stage-wise rollout on held-out -# scenarios. Two modes: -# - **Target feedback** (`policy_state=:target`): the policy receives its own -# predicted target as input, matching DE training semantics. -# - **Realized feedback** (`policy_state=:realized`): the policy receives the -# realized state from the solver, matching deployment semantics. -# -# The **target-violation share** measures how much cost comes from the slack -# penalty rather than actual operations — it should be small (``\le 5\%``) for -# a well-trained policy. - -# ```julia -# rollout_eval = RolloutEvaluation( -# subproblems, state_params_in, state_params_out, initial_state, eval_scenarios; -# stride=1, policy_state=:realized, -# ) -# rollout_eval(1, models) -# println("Operational cost: ", rollout_eval.last_objective_no_deficit) -# println("Violation share: ", rollout_eval.last_violation_share) -# ``` - -# ## SDDP baseline -# -# For comparison, we also train an SDDP policy using -# [SDDP.jl](https://github.com/odow/SDDP.jl) with **inconsistent -# formulations**: a convex SOC-WR relaxation for the backward pass -# (cut generation) and the nonconvex ACP formulation for the forward -# pass (simulation). This is a pragmatic approach when the true problem -# (AC-OPF) is nonconvex — SDDP requires convexity for valid cuts, so a -# convex relaxation approximates the value function while the forward pass -# evaluates under the true physics. -# -# The SDDP policy is trained for up to 2000 iterations and the learned -# cuts are saved to a JSON file, which can be loaded to simulate the -# policy under the ACP formulation. - -# ## Results -# -# The plots below compare the TS-DDR and TS-LDR training formulations and -# the SDDP baseline on the Bolivia case. Training curves, out-of-sample -# cost distributions, reservoir volume trajectories, and thermal generation -# profiles are shown. -# -# ### Training convergence (TS-DDR methods) -# -# ![Training convergence](../assets/hydro_training_convergence.png) -# -# ### Out-of-sample cost (TS-DDR methods) -# -# ![Out-of-sample cost comparison](../assets/hydro_cost_comparison.png) -# -# ### Target-violation share (TS-DDR methods) -# -# ![Violation share](../assets/hydro_violation_share.png) -# -# ### Reservoir volume comparison (all methods) -# -# ![Volume comparison](../assets/hydro_volume_comparison.png) -# -# ### Thermal generation comparison (all methods) -# -# ![Generation comparison](../assets/hydro_generation_comparison.png) -# -# ### Summary -# -# | Method | Policy | Mean Cost | Std | N | -# |:-------|:------:|----------:|----:|--:| -# | TS-DDR (DE) | LSTM | 325 540 | 6 266 | 100 | -# | TS-DDR (DE, anneal) | LSTM | 324 445 | 6 134 | 100 | -# | TS-DDR (shooting w=12) | LSTM | 323 289 | 5 593 | 100 | -# | TS-DDR (shooting w=12, anneal) | LSTM | 322 812 | 6 081 | 100 | -# | TS-DDR (stage-wise, anneal) | LSTM | 321 543 | 6 214 | 100 | -# | SDDP (SOC-WR / ACP) | cuts | 303 684 | — | 100 | -# -# All three TS-DDR methods with penalty annealing converge to similar -# costs (321K–325K). SDDP trains on 126 stages (96 + 30 margin). -# -# !!! note "Preliminary results" -# These numbers reflect the current default training scripts. -# They will be updated as the package evolves. diff --git a/docs/src/examples/hydro.md b/docs/src/examples/hydro.md deleted file mode 100644 index 6c23019..0000000 --- a/docs/src/examples/hydro.md +++ /dev/null @@ -1,488 +0,0 @@ -```@meta -EditURL = "hydro.jl" -``` - -# Hydropower Scheduling - -This example trains target-setting decision rules for the Bolivia -long-term hydrothermal dispatch (LTHD) problem — both **TS-DDR** (deep, -LSTM-based) and **TS-LDR** (linear) — and compares them against an SDDP -baseline with inconsistent formulations. - -The Bolivia system has **10 hydro plants**, **96 monthly stages**, and -**AC power flow** constraints. Inflow uncertainty is sampled from 47 -historical scenarios. - -## Overview of the TS-DDR approach - -Classical stochastic programming (e.g., SDDP) constructs piecewise-linear -value-function approximations. TS-DDR takes a different route: a neural -network policy ``\pi_\theta`` maps observations to **target states**, and a -projection subproblem at each stage enforces physical feasibility while -tracking those targets as closely as possible. - -The key insight is that the gradient of the projection subproblem with -respect to the target parameters is available through Lagrange duality -(or equivalently, implicit differentiation of the KKT conditions). -This avoids differentiating through the full optimization solver. - -## Problem formulation - -At each stage ``t``, the operator observes inflows ``w_t`` and the current -reservoir state ``x_{t-1}``. The policy predicts target volumes: - -```math -\hat{x}_t = \pi_\theta(w_{1:t},\, x_{t-1}). -``` - -A stage subproblem projects onto the feasible set: - -```math -\begin{aligned} -q_t(x_{t-1},\, w_t;\; \hat{x}_t) - \;=\; - \min_{x_t, u_t, \delta_t} - \quad & - c_t(x_t, u_t) + C_\delta\, \|\delta_t\| \\ -\text{s.t.}\quad - & x_t = x_{t-1} + w_t - \text{turbined}_t - \text{spilled}_t, - && \text{(reservoir balance)} \\ - & x_t + \delta_t = \hat{x}_t, - && : \lambda_t \quad \text{(target constraint)} \\ - & \text{AC-OPF}(u_t), - && \text{(power flow)} \\ - & x_t \in [0, \bar{x}],\; u_t \ge 0. -\end{aligned} -``` - -The slack variable ``\delta_t`` absorbs infeasible targets; ``\lambda_t`` is -the dual multiplier that provides the gradient signal. - -## Gradient computation: the envelope theorem - -By the envelope theorem, the sensitivity of the optimal value with respect -to the target parameter is simply the dual: - -```math -\frac{\partial q_t}{\partial \hat{x}_t} -\;=\; -\lambda_t. -``` - -Combined with backpropagation through the policy network, the full gradient -of the expected cost is: - -```math -\nabla_\theta \mathbb{E}[Q] -\;\approx\; -\frac{1}{S} \sum_{s=1}^{S} \sum_{t=1}^{T} - \lambda_t^s \odot \nabla_\theta \hat{x}_t^s(\theta), -``` - -where ``S`` is the number of sampled trajectories per batch and ``\odot`` -denotes elementwise multiplication. - -## Problem setup - -The JuMP subproblems are built from a MOF file (exported from PowerModels.jl) -plus hydro data (reservoir limits, inflow scenarios). Each subproblem contains: -- AC optimal power flow constraints -- Reservoir balance: `vol_out = vol_in + inflow - turbined - spilled` -- Target-slack deficit variables penalizing deviation from the policy's targets - -The helper `build_hydropowermodels` reads the case data, creates one JuMP model -per stage, and parameterizes the initial volumes and inflows so they can be set -at each training sample. - -````@example hydro -using DecisionRules -using JuMP, DiffOpt, Ipopt -using Flux -using Statistics, Random -```` - -Load the problem builder (reads MOF + hydro JSON + inflow CSV). - -```julia -include("load_hydropowermodels.jl") -``` - -## Building the stage-wise subproblems - -Each subproblem is wrapped with `DiffOpt.diff_optimizer` so that Lagrange duals -and implicit sensitivities are available for training. - -```julia -diff_optimizer = () -> DiffOpt.diff_optimizer( - optimizer_with_attributes(Ipopt.Optimizer, "print_level" => 0, "linear_solver" => "mumps") -) - -subproblems, state_params_in, state_params_out, uncertainty_samples, initial_state, max_volume = - build_hydropowermodels( - "bolivia", "ACPPowerModel.mof.json"; - num_stages=96, - optimizer=diff_optimizer, - penalty_l1=:auto, penalty_l2=:auto, - ) -``` - -## Policy architecture - -The policy is a [`StateConditionedPolicy`](@ref) with two components: - -1. **Encoder** — a stack of LSTM cells that processes only the uncertainty - (inflow) sequence, capturing temporal dependencies across stages. -2. **Combiner** — a Dense layer that merges the encoded uncertainty with the - previous state to produce the next target. - -At each stage the policy receives ``[w_t;\; x_{t-1}]`` and outputs -target reservoir volumes ``\hat{x}_t``: - -``` - ┌─────────┐ ┌────────────────┐ ┌──────────────┐ - │ w_t │─────▶│ LSTM encoder │─────▶│ │ - └─────────┘ └────────────────┘ │ Dense │──▶ x̂_t - ┌─────────┐ │ combiner │ - │ x_{t-1} │─────────────────────────────▶│ │ - └─────────┘ └──────────────┘ -``` - -The LSTM carries hidden state across stages, giving the policy memory of -past inflows. The activation is `sigmoid` (bounding outputs to ``[0,1]``, -which is then scaled by the feasibility mapping). - -```julia -models = state_conditioned_policy( - num_uncertainties, num_hydro, num_hydro, [128, 128]; - activation=sigmoid, encoder_type=Flux.LSTM, -) -``` - -## TS-LDR: Linear Decision Rules - -As a baseline, we also train a **linear** policy (TS-LDR). This uses -`dense_multilayer_nn` with identity activation — a composition of linear -layers equivalent to a single affine map: - -```math -\hat{x}_t = W [w_{1:t};\; x_{t-1}] + b. -``` - -TS-LDR uses the same target-setting framework and training pipeline as -TS-DDR. The only difference is the policy class: linear maps have fewer -parameters and cannot capture nonlinear inflow patterns, but they are a -natural baseline from the classical LDR literature. - -```julia -num_inputs = DecisionRules.policy_input_dim(num_uncertainties, num_hydro) -models = dense_multilayer_nn(num_inputs, num_hydro, [64, 64]; activation=identity) -``` - -## Training pipeline 1: Deterministic Equivalent - -The deterministic equivalent (DE) couples all 96 stages into a **single NLP** -for each sampled trajectory. This is the most direct formulation: the policy -generates the full target trajectory ``\hat{x}_{1:T}`` in one forward pass, -and a single coupled solve determines all realized states simultaneously. - -### How it works - -``` - ┌──────────────────────────────────────────────────────────┐ - │ For each sampled trajectory w_{1:T}: │ - │ │ - │ 1. Forward pass: x̂_{1:T} = π_θ(w_{1:T}, x_0) │ - │ │ - │ 2. Solve coupled NLP: │ - │ min Σ_t c_t(x_t, u_t) + C_δ Σ_t ‖δ_t‖ │ - │ s.t. dynamics + AC-OPF for ALL stages simultaneously │ - │ x_t + δ_t = x̂_t(θ) ∀t (target constraint) │ - │ │ - │ 3. Read duals λ_t of target constraints │ - │ Gradient: Σ_t λ_t ⊙ ∇_θ x̂_t(θ) │ - └──────────────────────────────────────────────────────────┘ -``` - -### Mathematical formulation - -```math -\begin{aligned} -Q(w;\, \theta) - \;=\; - \min_{\{x_t, u_t, \delta_t\}_{t=1}^{T}} - \quad & - \sum_{t=1}^{T} c_t(x_t, u_t) - + C_\delta \sum_{t=1}^{T} \|\delta_t\| \\ -\text{s.t.}\quad - & x_t = T_t(w_t,\, u_t,\, x_{t-1}), - && t=1,\ldots,T \\ - & x_t + \delta_t = \hat{x}_t(\theta), - && : \lambda_t,\quad t=1,\ldots,T \\ - & h_t(x_t, u_t) \ge 0, - && t=1,\ldots,T -\end{aligned} -``` - -The gradient is exact by the envelope theorem: - -```math -\nabla_\theta Q -\;=\; -\sum_{t=1}^{T} -\lambda_t \odot \nabla_\theta \hat{x}_t(\theta). -``` - -**Advantages**: strongest gradient signal — full cross-stage coupling -captures how a target at stage 3 affects costs at stage 50. - -**Disadvantage**: the NLP has ``96 \times (\text{AC-OPF variables})`` -decision variables; the policy generates targets without seeing realized -states (open-loop target generation). - -```julia -det_equivalent, uncertainty_samples_det = DecisionRules.deterministic_equivalent!( - det_model, subproblems_de, state_params_in, state_params_out, - Float64.(initial_state), uncertainty_samples, -) - -train_multistage( - models, initial_state, det_equivalent, - state_params_in, state_params_out, uncertainty_samples; - num_batches=4000, optimizer=Flux.Adam(), - penalty_schedule=[(1,100,0.1), (101,210,1.0), (211,300,10.0), (301,4000,30.0)], -) -``` - -## Training pipeline 2: Stage-wise Decomposition (Single Shooting) - -Stage-wise decomposition solves one subproblem per stage sequentially. -Unlike the DE, the policy operates in **closed loop**: after each stage -solve, the realized state ``x_t`` (not the predicted target) is fed back -as input to the next stage. - -### How it works - -``` - ┌─────────────────────────────────────────────────────────────┐ - │ For each sampled trajectory w_{1:T}: │ - │ │ - │ x_0 = initial state │ - │ for t = 1, ..., T: │ - │ x̂_t = π_θ(w_t, x_{t-1}) ← predict target │ - │ solve stage-t subproblem ← project to feasible│ - │ x_t = realized state from solver ← closed-loop │ - │ accumulate c_t + C_δ ‖δ_t‖ │ - │ │ - │ Gradient: chain rule through all stage solves │ - └─────────────────────────────────────────────────────────────┘ -``` - -### Gradient chain - -The gradient must account for how the realized state at stage ``t`` -depends on the targets at all earlier stages. By the chain rule: - -```math -\frac{\partial Q}{\partial \hat{x}_t} -\;=\; -\lambda_t -+ \sum_{k>t} - \frac{\partial q_k}{\partial x_{k-1}} - \cdot \prod_{j=t+1}^{k-1} - \frac{\partial x_j}{\partial x_{j-1}} - \cdot \frac{\partial x_t}{\partial \hat{x}_t}. -``` - -In practice, automatic differentiation (Zygote + ChainRules `rrule`s -defined on each stage solve) handles this chain automatically. -The `rrule` for each stage solve reads the dual ``\lambda_t`` for the -target constraint and uses DiffOpt's implicit differentiation for the -state-transition sensitivities. - -**Advantages**: closed-loop — the policy sees realized states, matching -deployment semantics. Each solve is small (single-stage AC-OPF). - -**Disadvantage**: gradients weaken over long horizons because the -chain rule multiplies many Jacobians; sequential solve prevents -parallelism. - -```julia -train_multistage( - models, initial_state, subproblems, - state_params_in, state_params_out, uncertainty_samples; - num_batches=3000, optimizer=Flux.Adam(), - penalty_schedule=:default_annealed, -) -``` - -## Training pipeline 3: Multiple Shooting - -Multiple shooting partitions the ``T``-stage horizon into ``K`` windows of -``W`` stages each. Within each window, a local deterministic equivalent -couples the stages (strong gradient signal). Between windows, the realized -end-state is passed to the next window (closed-loop continuity). - -### How it works - -``` - ┌────────────────────────────────────────────────────────────────┐ - │ Partition T=96 stages into K=⌈96/12⌉=8 windows of W=12 │ - │ │ - │ x_0 = initial state │ - │ for k = 1, ..., K: │ - │ stages = [(k-1)W+1, ..., kW] │ - │ x̂_{stages} = π_θ(w_{stages}, x_{start_k}) │ - │ solve window-k DE (12-stage coupled NLP) │ - │ x_{end_k} = realized end-state from window solve │ - │ x_{start_{k+1}} = x_{end_k} │ - │ │ - │ Gradient: │ - │ Within window: duals from the coupled solve (like full DE) │ - │ Across windows: DiffOpt chain rule through end-states │ - └────────────────────────────────────────────────────────────────┘ -``` - -### Gradient structure - -Let ``Q_k`` be the cost of window ``k``. The total cost is -``Q = \sum_k Q_k``. Within a window, the gradient is identical to the -DE case (duals of the target constraints in the coupled model). Across -windows, the chain rule threads through the realized end-state: - -```math -\frac{dQ}{d\theta} -\;=\; -\sum_{k=1}^{K} -\left( - \frac{\partial Q_k}{\partial \hat{x}_k} - \cdot \frac{\partial \hat{x}_k}{\partial \theta} - \;+\; - \frac{\partial Q_k}{\partial x_{\text{start}_k}} - \cdot \frac{d x_{\text{start}_k}}{d\theta} -\right), -``` - -where ``\frac{d x_{\text{start}_k}}{d\theta}`` involves the chain -through all prior windows via ``x_{\text{end}_{k-1}}``. - -**Advantages**: balances gradient quality (12-stage coupling) with -tractability (8 small DEs instead of one large one); inter-window -chain provides some closed-loop signal. - -**Disadvantage**: window boundaries introduce gradient discontinuities; -the full-horizon coupling is weaker than the single DE. - -```julia -windows = DecisionRules.setup_shooting_windows( - subproblems, state_params_in, state_params_out, - Float64.(initial_state), uncertainty_samples; - window_size=12, - model_factory=() -> DiffOpt.nonlinear_diff_model(ipopt_attrs), -) - -train_multiple_shooting( - models, initial_state, windows, () -> uncertainty_samples; - num_batches=3000, optimizer=Flux.Adam(), - penalty_schedule=:default_annealed, -) -``` - -## Penalty annealing - -The target penalty ``C_\delta`` controls the trade-off between following -the policy's targets and minimizing operational cost. DecisionRules -supports a **penalty annealing schedule** that ramps the penalty multiplier -during training: - -| Phase | Multiplier | Purpose | -|:------|:----------:|:--------| -| Warmup | ``0.1 \times C_\delta`` | Let the policy explore freely | -| Nominal | ``1.0 \times C_\delta`` | Standard training | -| Tighten | ``10.0 \times C_\delta`` | Sharpen target tracking | -| Lock | ``30.0 \times C_\delta`` | Final precision | - -This is activated with `penalty_schedule=:default_annealed` or by passing -an explicit list of `(start_iter, end_iter, multiplier)` tuples. - -## Evaluation - -After training, we evaluate the policy using stage-wise rollout on held-out -scenarios. Two modes: -- **Target feedback** (`policy_state=:target`): the policy receives its own - predicted target as input, matching DE training semantics. -- **Realized feedback** (`policy_state=:realized`): the policy receives the - realized state from the solver, matching deployment semantics. - -The **target-violation share** measures how much cost comes from the slack -penalty rather than actual operations — it should be small (``\le 5\%``) for -a well-trained policy. - -```julia -rollout_eval = RolloutEvaluation( - subproblems, state_params_in, state_params_out, initial_state, eval_scenarios; - stride=1, policy_state=:realized, -) -rollout_eval(1, models) -println("Operational cost: ", rollout_eval.last_objective_no_deficit) -println("Violation share: ", rollout_eval.last_violation_share) -``` - -## SDDP baseline - -For comparison, we also train an SDDP policy using -[SDDP.jl](https://github.com/odow/SDDP.jl) with **inconsistent -formulations**: a convex SOC-WR relaxation for the backward pass -(cut generation) and the nonconvex ACP formulation for the forward -pass (simulation). This is a pragmatic approach when the true problem -(AC-OPF) is nonconvex — SDDP requires convexity for valid cuts, so a -convex relaxation approximates the value function while the forward pass -evaluates under the true physics. - -The SDDP policy is trained for up to 2000 iterations and the learned -cuts are saved to a JSON file, which can be loaded to simulate the -policy under the ACP formulation. - -## Results - -The plots below compare the TS-DDR and TS-LDR training formulations and -the SDDP baseline on the Bolivia case. Training curves, out-of-sample -cost distributions, reservoir volume trajectories, and thermal generation -profiles are shown. - -### Training convergence (TS-DDR methods) - -![Training convergence](../assets/hydro_training_convergence.png) - -### Out-of-sample cost (TS-DDR methods) - -![Out-of-sample cost comparison](../assets/hydro_cost_comparison.png) - -### Target-violation share (TS-DDR methods) - -![Violation share](../assets/hydro_violation_share.png) - -### Reservoir volume comparison (all methods) - -![Volume comparison](../assets/hydro_volume_comparison.png) - -### Thermal generation comparison (all methods) - -![Generation comparison](../assets/hydro_generation_comparison.png) - -### Summary - -| Method | Policy | Mean Cost | Std | N | -|:-------|:------:|----------:|----:|--:| -| TS-DDR (DE) | LSTM | 325 540 | 6 266 | 100 | -| TS-DDR (DE, anneal) | LSTM | 324 445 | 6 134 | 100 | -| TS-DDR (shooting w=12) | LSTM | 323 289 | 5 593 | 100 | -| TS-DDR (shooting w=12, anneal) | LSTM | 322 812 | 6 081 | 100 | -| TS-DDR (stage-wise, anneal) | LSTM | 321 543 | 6 214 | 100 | -| SDDP (SOC-WR / ACP) | cuts | 303 684 | — | 100 | - -All three TS-DDR methods with penalty annealing converge to similar -costs (321K–325K). SDDP trains on 126 stages (96 + 30 margin). - -!!! note "Preliminary results" - These numbers reflect the current default training scripts. - They will be updated as the package evolves. - diff --git a/docs/src/gpu_acceleration.md b/docs/src/gpu_acceleration.md index 5cbf498..efeea08 100644 --- a/docs/src/gpu_acceleration.md +++ b/docs/src/gpu_acceleration.md @@ -138,21 +138,6 @@ The key requirements are: 3. **Return** a struct with fields `.core`, `.model`, `.horizon`, and `.target_con_range`. -The `HydroPowerModels` example in DecisionRulesExa.jl demonstrates this -pattern for a full AC-OPF problem with reservoir dynamics: - -```julia -# In examples/HydroPowerModels/hydro_power_exa.jl -prob = build_hydro_de( - data; - num_stages = 96, - backend = CUDABackend(), - formulation = :ac_polar, - deficit_cost = 1e5, - target_penalty = :auto, -) -``` - ## Parallel GPU solves When training samples are independent, multiple NLP instances can be @@ -236,18 +221,179 @@ target-deficit penalty, and target-violation share. | Stage-wise decomposition | — | JuMP only | | Multiple shooting | — | JuMP only | -## Full example: HydroPowerModels - -The `examples/HydroPowerModels/` directory in DecisionRulesExa.jl contains -a complete AC-OPF hydrothermal scheduling example for the Bolivia test case -— the same problem solved by DecisionRules.jl in the -[Hydropower Scheduling](@ref) tutorial. It demonstrates: - -- Parsing PowerModels.jl network data and hydro reservoir parameters -- Building a multi-stage deterministic-equivalent NLP in ExaModels - (DC or AC polar OPF formulations) -- L1 + L2 penalty on target slack (δ⁺/δ⁻ splitting for smooth NLP) -- GPU training with parallel MadNLP solves -- Warm-start caching to prevent cascade solver failures -- Penalty and sample-count annealing schedules -- W&B metric logging +## Embedded deterministic equivalent + +The standard `DeterministicEquivalentProblem` treats the policy's target +trajectory as an external parameter: the training loop generates +``\hat{x}_{1:T}`` outside the NLP and passes it in via `set_targets!`. +This is **open-loop** — the policy does not see the realized states +from the coupled solve. + +`EmbeddedDeterministicEquivalentProblem` embeds the policy *inside* +the NLP via a `VectorNonlinearOracle`. The NLP constraint becomes: + +```math +\pi_\theta(w_t,\, x_{t-1}^*) - x_t - \delta_t = 0 \quad \forall t +``` + +where ``x_{t-1}^*`` is the solver's realized state. This is +**closed-loop**: the policy sees realized states from the coupled solve, +and the duals ``\lambda_t`` reflect the joint (policy + physics) system. + +```julia +prob = build_embedded_deterministic_equivalent( + policy; + horizon = T, + nx = nx, + nu = nu, + nw = nw, + dynamics_eq = my_dynamics, + stage_cost = my_cost, + backend = CUDABackend(), +) + +train_tsddr_embedded( + policy, x0, prob, sampler; + num_batches = 500, + num_train_per_batch = 4, + optimizer = Flux.Adam(1f-3), + madnlp_kwargs = (print_level = MadNLP.ERROR, tol = 1e-6), +) +``` + +The oracle closures capture the policy **by reference** — updating Flux +parameters between solves automatically changes the NLP without +rebuilding it. Use `invalidate_policy_cache!` if your oracle caches +policy-dependent intermediates. + +### Strict reachable targets + +When the policy is guaranteed to produce feasible targets (e.g., via a +reachable-set mapping), the slack variables ``\delta_t`` can be removed +entirely. This is strict mode: target constraints are hard equalities, the duals +are pure shadow prices, and there is no target penalty to tune. + +There are two safe strict paths in the hydro example. + +**Embedded strict DE** evaluates the policy inside the NLP against realized +reservoir decision variables: + +```julia +prob = build_embedded_hydro_de(policy, power_data, hydro_data, T; + formulation = :ac_polar, + strict_targets = true, +) +``` + +Its constraint is simply ``x_t = \pi_\theta(w_t, x_{t-1}^*)``. Because the +policy receives the realized previous state, a reachable-set map can guarantee +that the next strict equality is feasible. The current embedded hydro oracle +hand-codes the reachable policy Jacobian for the default single Dense head; use +`combiner_layers = Int[]` unless that oracle is extended. + +**Regular strict DE** keeps the policy outside the NLP but rolls out targets +from the known initial state: + +```math +\hat{x}_0 = x_0,\qquad +\hat{x}_t = \pi_\theta(w_t, \hat{x}_{t-1}). +``` + +If the policy returns ``\hat{x}_t \in R(\hat{x}_{t-1}, w_t)`` at every stage, +then the entire strict DE target trajectory is feasible by induction. The solve +then enforces ``x_t = \hat{x}_t`` for every stage, so the realized state path is +exactly the reachable target path. + +For cascaded hydro systems (upstream→downstream water flows), the per-unit +reachable set depends on the upstream unit's target at the same stage. The +`HydroReachablePolicy` handles this by computing initial targets for all units +using worst-case upstream bounds (``K \times \bar{q}_u``), then applying a +**cascade clamping** step that adjusts downstream targets based on the actual +upstream release implied by the upstream target. For turn+spill connections +the full release is available; for turn-only connections only +``\min(K \bar{q}_u, R_u)`` reaches the downstream unit. This clamping is +`@non_differentiable` — gradient flows through for non-clamped targets, and +is zero for clamped ones (correct projected-gradient signal). + +The ExaModels strict DE also applies cascade clamping in `prepare_solve!` as a +safety net: before each NLP solve, the reservoir parameter values are checked +and clamped stage-by-stage to ensure cascade feasibility. + +The Exa hydro script exposing this path is: + +```bash +DR_ENCODER_LAYERS=128,128 \ +DR_HEAD_LAYERS=128,128 \ +julia --project -t auto train_hydro_exa_strict.jl +``` + +`DR_ENCODER_LAYERS` controls recurrence over inflows. `DR_HEAD_LAYERS` controls +the nonrecurrent state-conditioned target head; it can be used to make the +reachable policy nonlinear in the current reservoir state without adding state +recurrence. + +## Sequential rollout evaluation + +`train_tsddr` solves the full deterministic equivalent in one shot. For +deployment diagnostics, DecisionRulesExa.jl provides `RolloutEvaluation`, which +solves a one-stage ExaModels problem sequentially over a materialized scenario: + +```julia +eval = RolloutEvaluation( + stage_problem, + x0, + eval_scenarios; + horizon = T, + n_uncertainty = nw, + set_stage_parameters! = my_setter!, + realized_state = my_state_reader, + policy_state = :realized, +) +``` + +This mirrors deployment semantics: the policy can be evaluated with the +realized previous state (`policy_state = :realized`) or with its previous target +(`policy_state = :target`) to match regular-DE target-generation semantics. + +## Critic control variate + +`train_tsddr` optionally trains a scalar critic ``C(w, \hat{x})`` that +provides a learned control variate for the dual gradient signal. The +critic does not replace the NLP solve — dual multipliers remain the +primary actor gradient. The critic reduces gradient variance by +subtracting a correlated baseline. + +```julia +critic = Chain(Dense(input_dim => 128, tanh), Dense(128 => 128, tanh), Dense(128 => 1)) + +cv = ScalarCriticControlVariate(critic; + featurizer = default_critic_featurizer, + value_loss_weight = 1.0, + gradient_loss_weight = 0.0, +) + +critic_target = RolloutCriticTarget(stage_problem; + horizon = T, + n_uncertainty = nw, + set_stage_parameters! = my_setter!, + realized_state = my_state_reader, + policy_state = :target, +) + +train_tsddr(policy, x0, prob, prob.p_x0, prob.p_target, prob.p_w, sampler; + control_variate = cv, + critic_training_target = critic_target, + actor_gradient_mode = :control_variate, + critic_cv_weight = 1.0, + critic_optimizer = Flux.Adam(1f-3), +) +``` + +Two actor modes are supported: + +- `:control_variate` — subtracts ``\nabla_{\hat{x}} C`` from the dual + signal and adds it back as a differentiable surrogate. Unbiased when + the critic is exact; reduces variance otherwise. +- `:surrogate` — blends dual and critic actor gradients via explicit + weights (`dual_actor_weight`, `critic_actor_weight`). Useful when raw + duals are noisy, but no longer strictly unbiased. diff --git a/docs/src/guide/getting_started.md b/docs/src/guide/getting_started.md new file mode 100644 index 0000000..56159f1 --- /dev/null +++ b/docs/src/guide/getting_started.md @@ -0,0 +1,115 @@ +# Getting started + +```@meta +CurrentModule = DecisionRules +``` + +## Installation + +```julia +using Pkg +Pkg.add("DecisionRules") +``` + +DecisionRules.jl builds policies with [Flux.jl](https://fluxml.ai) and +subproblems with [JuMP](https://jump.dev); training requires a +DiffOpt-compatible solver for the stage problems (Ipopt for smooth NLPs, +HiGHS for LPs/MIPs). For GPU-accelerated training of large NLPs, install +the companion package +[DecisionRulesExa.jl](https://github.com/LearningToOptimize/DecisionRulesExa.jl) +(see [GPU Acceleration with DecisionRulesExa.jl](@ref)). + +## Anatomy of a training run + +Every TS-DDR training run assembles the same five ingredients: + +1. **Stage subproblems** — one JuMP model per stage, wrapped with + `DiffOpt.diff_optimizer` so Lagrange duals and sensitivities are + available. The incoming state, the uncertainty, and the policy's + target state enter as *parameters*. +2. **A policy** — a Flux model mapping ``[w_t;\, x_{t-1}]`` to a target + state ``\hat{x}_t`` (see [Target-state policies](@ref)). +3. **An uncertainty sampler** — how trajectories ``w_{1:T}`` are drawn; + the three supported formats (independent pools, joint-scenario pools, + trajectory samplers) are the subject of [Uncertainty Sampling](@ref). +4. **A training formulation** — deterministic equivalent, stage-wise, + multiple shooting, or strict (see + [Three training formulations](@ref) and + [Strict mode: penalty-free gradient signal](@ref)). +5. **An evaluation protocol** — out-of-sample stage-wise rollout via + [`RolloutEvaluation`](@ref), with target- or realized-state feedback + (see [Evaluation semantics](@ref)). + +## Quick start + +```julia +using DecisionRules, JuMP, DiffOpt, Flux, Ipopt + +# Build per-stage subproblems in JuMP (DiffOpt-enabled) +# subproblems, state_params_in, state_params_out, uncertainty_samples, initial_state = ... + +# Define a policy: maps [uncertainty; state] → target state +policy = Chain( + Dense(policy_input_dim(num_uncertainties, num_states), 64, relu), + Dense(64, num_states), +) + +# Train via stage-wise decomposition +train_multistage( + policy, initial_state, subproblems, + state_params_in, state_params_out, uncertainty_samples; + num_batches=100, optimizer=Flux.Adam(1e-3), +) +``` + +## Choosing a training formulation + +| Formulation | Horizon coupling | Gradient source | +|:---|:---|:---| +| **Deterministic Equivalent** | Full horizon, one large NLP | Duals on the coupled problem | +| **Stage-wise (single shooting)** | Sequential rollout | Duals + DiffOpt per stage | +| **Multiple Shooting** | Windowed sub-horizons | DiffOpt per window, continuity penalties | +| **Strict subproblems** | Sequential rollout, no slack | Pure shadow-price duals | + +As a rule of thumb: + +- start with **stage-wise** training — closed-loop, smallest solves, + fewest assumptions; +- move to the **deterministic equivalent** (or its GPU implementation) + when the per-stage solves are large and horizon-coupled gradient signal + pays off; +- use **multiple shooting** as the middle ground on long horizons; +- switch to **strict** mode whenever you can construct a + feasibility-guaranteeing policy — one that bounds its output to the + one-stage reachable set ``R(x, w)`` of the dynamics. Constructing ``R`` + is problem-specific (cheap for resource-balance dynamics with box + bounds; the [Hydropower Scheduling](@ref) case study builds a complete + instance). Strict mode eliminates the target-slack penalty and its + tuning entirely, and the dual ``\lambda_t`` becomes the pure shadow + price. + +## Robustness and hardware + +- Solver or differentiation failures during training are handled by the + pluggable [gradient fallback](@ref "Gradient Fallback") system — + by default a failed iteration logs a warning and is skipped. +- Problems with large stage NLPs train an order of magnitude + faster on GPU through + [DecisionRulesExa.jl](@ref "GPU Acceleration with DecisionRulesExa.jl"), + which implements the strict deterministic equivalent with + ExaModels + MadNLP/cuDSS. + +## Where to go next + +- Theory: + [multistage stochastic optimization](@ref "Multistage stochastic optimization"), + [the TS-DDR framework](@ref "The TS-DDR framework"), + [SDDP and inconsistent formulations](@ref "Stochastic dual dynamic programming"), + [extensions](@ref "Extensions: mixed gradients, critics, and risk"). +- Worked problems: the hydrothermal case study + ([problem](@ref "The long-term hydrothermal planning problem"), + [instance](@ref "The Bolivian interconnected system"), + [walkthrough](@ref "Hydropower Scheduling")), + [rocket control](@ref "Rocket Control"), and + [stochastic lot-sizing](@ref "Stochastic Lot-Sizing with Fixed Ordering Costs"). +- [API Reference](@ref): every exported symbol. diff --git a/docs/src/index.md b/docs/src/index.md index 7051de0..a4782c8 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -4,27 +4,60 @@ CurrentModule = DecisionRules ``` -DecisionRules.jl trains parametric decision rules through multi-stage optimization, -implementing the **Two-Stage Deep Decision Rules (TS-DDR)** framework from -[arXiv:2405.14973](https://arxiv.org/abs/2405.14973). - -## How it works - -In multi-stage stochastic control, the feasible action at each stage comes from solving -a constrained optimization problem (OPF, MPC, hydrothermal dispatch, …). Rather than -outputting actions directly, the neural-network policy outputs **target states**. -An optimization subproblem then projects these targets onto the feasible set defined by -dynamics and constraints. Lagrange duals and implicit differentiation (via -[DiffOpt.jl](https://github.com/jump-dev/DiffOpt.jl)) provide the gradient signal to -update the policy end-to-end. - -Three training formulations are supported: - -| Formulation | Horizon coupling | Gradient source | -|:---|:---|:---| -| **Deterministic Equivalent** | Full horizon, one large NLP | Duals on the coupled problem | -| **Stage-wise (single shooting)** | Sequential rollout | Duals + DiffOpt per stage | -| **Multiple Shooting** | Windowed sub-horizons | DiffOpt per window, continuity penalties | +DecisionRules.jl trains parametric decision rules — from affine policies +to deep recurrent networks — for multistage stochastic optimization +problems whose actions come from constrained optimization subproblems +(optimal power flow, MPC, inventory control, …). It implements the +**Two-Stage Deep Decision Rules (TS-DDR)** framework of +[arXiv:2405.14973](https://arxiv.org/abs/2405.14973): the policy outputs +**target states**, a projection subproblem restores exact feasibility, and +Lagrange duals (with implicit differentiation via +[DiffOpt.jl](https://github.com/jump-dev/DiffOpt.jl) where needed) provide +the end-to-end training gradient — no differentiation through solver +iterations, no feasibility violations at deployment. + +In the **strict** formulation, the target constraints are hard equalities +and the policy is built to emit only reachable targets: no slack, no +penalty hyperparameter, and the dual ``\lambda_t`` is the pure shadow +price of the target. A GPU companion package, +[DecisionRulesExa.jl](https://github.com/LearningToOptimize/DecisionRulesExa.jl), +trains the same policies through full-horizon deterministic equivalents +with ExaModels + MadNLP/cuDSS. + +## The documentation + +**Theory.** The +[multistage stochastic optimization problem](@ref "Multistage stochastic optimization") +and where decision rules sit among solution methods; +[the TS-DDR framework](@ref "The TS-DDR framework") — target-state +policies, dual gradients, the training formulations, and strict mode with +its reachability-based feasibility guarantee; +[stochastic dual dynamic programming](@ref "Stochastic dual dynamic programming"), +including the inconsistent-formulation variant for nonconvex stage problems and +the bound-versus-forward gap; and +[extensions](@ref "Extensions: mixed gradients, critics, and risk") — +score-function corrections for integer decisions, control-variate +critics, risk-averse objectives. + +**Package guide.** [Getting started](@ref); +[uncertainty sampling formats](@ref "Uncertainty Sampling"); +[gradient fallback](@ref "Gradient Fallback"); +[GPU acceleration](@ref "GPU Acceleration with DecisionRulesExa.jl"); +[API Reference](@ref). + +**Case studies.** The flagship is hydrothermal scheduling on the Bolivian +interconnected system, in three chapters — +[the planning problem](@ref "The long-term hydrothermal planning problem") +(multistage AC-OPF with cascaded reservoir dynamics), +[the instance](@ref "The Bolivian interconnected system") +(a real grid whose counter-cyclical, storage-critical operation sits in +the regime where convex value-of-water surrogates misprice the network), +and [the walkthrough](@ref "Hydropower Scheduling") +(strict TS-DDR on CPU and GPU against an SDDP baseline, under a paired +evaluation protocol). Two further studies, +[rocket control](@ref "Rocket Control") and +[stochastic lot-sizing](@ref "Stochastic Lot-Sizing with Fixed Ordering Costs"), +exercise continuous control and mixed-integer recourse. ## Installation @@ -33,32 +66,8 @@ using Pkg Pkg.add("DecisionRules") ``` -## Quick start - -```julia -using DecisionRules, JuMP, DiffOpt, Flux, Ipopt - -# Build per-stage subproblems in JuMP (DiffOpt-enabled) -# subproblems, state_params_in, state_params_out, uncertainty_samples, initial_state = ... - -# Define a policy: maps [uncertainty; state] → target state -policy = Chain( - Dense(policy_input_dim(num_uncertainties, num_states), 64, relu), - Dense(64, num_states), -) - -# Train via stage-wise decomposition -train_multistage( - policy, initial_state, subproblems, - state_params_in, state_params_out, uncertainty_samples; - num_batches=100, optimizer=Flux.Adam(1e-3), -) -``` - -See the [Algorithm](@ref) page for the mathematical formulation, the -[Uncertainty Sampling](@ref) guide for how to prepare your scenario data, the -[GPU Acceleration with DecisionRulesExa.jl](@ref) page for GPU-accelerated training, -and the examples for complete worked problems. +[Getting started](@ref) covers solver requirements, a quick-start example, +and how to choose among the training formulations. ## Citation diff --git a/docs/src/sampling.md b/docs/src/sampling.md index bacdfad..5e60a3b 100644 --- a/docs/src/sampling.md +++ b/docs/src/sampling.md @@ -307,7 +307,7 @@ maintainability. parameters from an uncertainty pool, discarding the scenario values. Used by `setup_shooting_windows` for multiple-shooting training. -## API Reference +## Docstrings ```@docs sample diff --git a/docs/src/theory/extensions.md b/docs/src/theory/extensions.md new file mode 100644 index 0000000..ee6997c --- /dev/null +++ b/docs/src/theory/extensions.md @@ -0,0 +1,165 @@ +# Extensions: mixed gradients, critics, and risk + +```@meta +CurrentModule = DecisionRules +``` + +The dual gradient of [The TS-DDR framework](@ref) is exact for smooth +subproblems and, over fresh samples, an unbiased estimator of the +expected-cost gradient. Two practical situations call for more: +**discrete decisions**, where the dual is blind to integer switches and a +score-function (REINFORCE) correction restores the missing signal, and +**small sample budgets**, where a control-variate critic cuts the +estimator's variance without moving its optimum. Both extensions, and a +risk-averse change-of-measure variant of the gradient, ship with the +package. + +!!! note "Scope" + The hydrothermal case study is smooth and trains with the pure strict + dual gradient — it uses none of these extensions. The score-function + correction is exercised in + [Stochastic Lot-Sizing with Fixed Ordering Costs](@ref), whose + fixed-charge (binary) ordering decisions are exactly the situation it + addresses. + +## Mixed gradient: score-function (REINFORCE) correction + +For problems with integer variables or non-smooth subproblems, the dual +gradient can be biased — it is local to a fixed integer assignment and cannot +see the effect of discrete switches (e.g., opening a setup variable). + +DecisionRules provides a **score-function (REINFORCE)** correction that mixes +the dual gradient with a model-free policy gradient estimated from stage-wise +rollouts under perturbed targets. + +### How the score-function estimator works + +1. **Perturb**: add Gaussian noise to the policy targets: + ``\tilde{x}_t = \hat{x}_t(\theta) + \delta_t``, where + ``\delta_t \sim \mathcal{N}(0, \sigma^2 I)``. + +2. **Rollout**: solve the stage-wise subproblems with the perturbed targets to + obtain realized costs ``R_m`` for ``m = 1, \ldots, M`` rollouts. These + rollouts solve the models exactly as built (MIPs stay MIPs), so the costs + reflect true integer-feasible decisions. + +3. **Advantage**: center the costs ``A_m = R_m - \bar{R}``. Because the mean + baseline ``\bar{R}`` is computed from the same ``M`` rollouts, mean-centering + reduces variance but introduces a small ``O(1/M)`` bias (effectively scaling + the estimator by ``(M-1)/M``) that vanishes as `num_rollouts` grows; a + leave-one-out baseline would be exactly unbiased. + +4. **Surrogate loss**: the differentiable scalar whose gradient recovers the + REINFORCE estimate: + +```math +L_{\text{sf}}(\theta) +\;=\; +\frac{1}{M} \sum_{m=1}^{M} + A_m + \sum_{t=1}^{T} + \left\langle + \frac{\delta_{m,t}}{\sigma^2},\; + \hat{x}_{t+1}(\theta) + \right\rangle. +``` + +This is the standard score-function estimator for Gaussian perturbations. +The key identity is +``\nabla_\theta \log p(\delta_t \mid \theta) = \delta_t / \sigma^2`` +for a Gaussian centered at ``\hat{x}_t(\theta)``. + +### Mixed gradient + +The final training gradient combines both signals: + +```math +\nabla L +\;=\; +\alpha\, \nabla L_{\text{dual}} ++ (1 - \alpha)\, \nabla L_{\text{sf}}, +``` + +where ``\alpha \in [0, 1]`` is the `dual_weight`. + +There are two separate solve paths in the mixed-gradient training loop: + +- **Dual path**: controlled by `integer_strategy`, which determines how local + dual information is read from the deterministic equivalent + (e.g., [`FixedDiscreteIntegerStrategy`](@ref) solves the MIP, fixes integers, + re-solves the LP, and reads LP duals). +- **Score-function path**: controlled by [`ScoreFunctionConfig`](@ref), which + owns separate rollout subproblems. These are solved exactly as built, and + their realized costs define the Monte Carlo score-function term. + +### Scheduled ramp-in + +A [`ScoreFunctionSchedule`](@ref) can ramp ``\alpha`` from 1 (pure dual) to +its final value over a warmup period. Let ``k`` be the current iteration and +``\rho_k = \operatorname{clip}((k - k_0) / r,\, 0,\, 1)``. The effective +score-function weight is ``\rho_k (1 - \alpha)``. + +This lets the DE dual gradient establish a good initial policy before +introducing the higher-variance REINFORCE signal. + +See the [Stochastic Lot-Sizing with Fixed Ordering Costs](@ref) example for a +complete worked example with integer variables and mixed gradients. + +## Variance reduction: control-variate critic + +The dual gradient over a batch of ``N`` sampled trajectories is the +sample-average + +```math +g \;=\; \frac{1}{N}\sum_{s=1}^{N}\sum_{t=1}^{T} + \bigl\langle \lambda^{s}_t,\; \partial \hat{x}^{s}_t/\partial\theta \bigr\rangle , +\qquad \lambda^{s}_t = \partial Q_s/\partial \hat{x}^{s}_t . +``` + +With fresh independent samples each step this is an **unbiased** estimator of +``\nabla_\theta\,\mathbb{E}[Q]`` for any ``N``. A small batch does not bias it — +it only inflates its **variance**, which sets the SGD noise floor and keeps the +policy short of the optimum. Rather than paying for a large ``N``, a +`ScalarCriticControlVariate` subtracts a learned, state-conditioned +baseline ``b^{s}_t = \nabla_{\hat{x}_t} C \approx \lambda^{s}_t`` and adds it back +as an independent-sample expectation: + +```math +g_{\text{cv}} \;=\; + \frac{1}{N}\sum_{s}\sum_t \bigl\langle \lambda^{s}_t - b^{s}_t,\; \partial\hat{x}^{s}_t/\partial\theta\bigr\rangle + \;+\; + \frac{1}{M}\sum_{j}\sum_t \bigl\langle b^{j}_t,\; \partial\hat{x}^{j}_t/\partial\theta\bigr\rangle . +``` + +**Unbiasedness.** The subtracted and added terms are two Monte-Carlo estimates of +the same expectation ``\mathbb{E}\bigl[\sum_t\langle b_t,\partial\hat{x}_t/\partial\theta\rangle\bigr]``, +so ``\mathbb{E}[g_{\text{cv}}] = \mathbb{E}[g] = \nabla_\theta\mathbb{E}[Q]``: the +critic **cannot move the optimum**. (If the add-back reuses the same samples with +``M=N``, the two terms cancel and the critic is a no-op — a fresh add-back batch, +`num_cheap_critic_samples_per_batch > 0`, is what activates it.) + +**Variance.** The reduction is governed by how well the baseline tracks the dual, + +```math +\text{Var reduction} \;\approx\; \frac{1}{1 - R^2}, \qquad +R^2 = \text{explained variance of } \lambda_t \text{ by } b_t , +``` + +so the baseline must be trained to **match the dual** (`gradient_loss_weight > 0`), +not only the scalar value (`value_loss_weight`): a value-only critic leaves +``\nabla_{\hat{x}} C`` unconstrained, ``R^2\approx 0``, and yields no reduction. The +control-variate critic is therefore a **sample-efficiency lever** — it reaches the +same risk-neutral optimum a much larger batch would, at a modest batch size. + +## Risk-averse extension (nested change-of-measure) + +The same per-stage critic supports a **risk-averse** objective by replacing the +expectation with a nested, time-consistent coherent risk measure (e.g. conditional +``\mathrm{CVaR}_\alpha``). Each stage weight becomes ``\Xi^{s}_t\,\lambda^{s}_t`` with +``\Xi^{s}_t = \prod_{u\le t}\zeta^{s}_u``, the causal product of per-node +change-of-measure densities ``\zeta_u`` (``\zeta\equiv 1`` recovers the risk-neutral +gradient above). Because each ``\zeta_u`` depends only on the distribution +*conditional* on stage ``u``, the policy never hedges against a tail already +precluded by realized uncertainty — the time-consistency property that a global +scenario re-weighting would violate. This targets tail cost at the expense of the +mean, and is a distinct objective from the risk-neutral formulation above. diff --git a/docs/src/theory/multistage.md b/docs/src/theory/multistage.md new file mode 100644 index 0000000..7cdb1f8 --- /dev/null +++ b/docs/src/theory/multistage.md @@ -0,0 +1,196 @@ +# Multistage stochastic optimization + +```@meta +CurrentModule = DecisionRules +``` + +Everything downstream — the TS-DDR framework, the SDDP baseline, the case +studies — is an attempt to solve one problem: sequential decision making +under uncertainty, with actions constrained by an optimization-level +feasible set. What follows fixes +notation, recalls the dynamic-programming view and why it is intractable +in general, and places **decision rules** among the classical solution +families; readers fluent in multistage stochastic programming can skim to +[Decision rules](@ref decision-rules-sec) and continue with +[The TS-DDR framework](@ref). + +## Problem statement + +Consider a sequential decision problem over a finite horizon of ``T`` +stages. At each stage ``t = 1, \ldots, T``: + +1. an exogenous **uncertainty realization** ``w_t \in \mathcal{W}_t`` is + revealed (river inflows, demands, prices, disturbances); +2. the decision maker, knowing the current **state** ``x_{t-1}`` and the + realization ``w_t`` (and, in general, the whole history + ``w_{1:t} = (w_1, \ldots, w_t)``), chooses a **control** + ``u_t`` and a next state ``x_t``; +3. the pair must satisfy the stage feasibility constraints, + ``(u_t, x_t) \in \mathcal{X}_t(x_{t-1}, w_t)``, which encode both the + **dynamics** (how the state evolves) and the **static constraints** of + the stage (a network, a budget, a capacity — whatever the application + imposes within a single period); +4. a **stage cost** ``c_t(x_t, u_t)`` is incurred. + +The objective is to choose a *policy* — a rule for making each decision +from the information available when it must be made — minimizing expected +total cost: + +```math +\min_{\pi \in \Pi} \; +\mathbb{E}_{w_{1:T}} \left[ \sum_{t=1}^{T} c_t\bigl(x_t^\pi, u_t^\pi\bigr) \right] +\qquad \text{s.t.} \quad +\bigl(u_t^\pi, x_t^\pi\bigr) \in \mathcal{X}_t\bigl(x_{t-1}^\pi, w_t\bigr) +\;\; \forall t, +``` + +where ``\Pi`` is the set of **nonanticipative** policies: ``u_t^\pi`` may +depend on ``w_{1:t}`` but not on future realizations ``w_{t+1:T}``. +Nonanticipativity is what makes the problem *stochastic control* rather +than a family of deterministic problems — every decision is a hedge +against a distribution of futures, committed before those futures are +revealed. + +Two structural features of this formulation deserve emphasis, because the +solution methods differ precisely in how they treat them: + +- **Intertemporal coupling through the state.** The only channel through + which stage ``t`` affects stage ``t+1`` is ``x_t``. A resource stored in + the state (water in a reservoir, inventory on a shelf, fuel in a tank) + has an *opportunity cost* — the expected future cost avoided by carrying + it forward — that no single-stage view can price. +- **Constrained actions.** The feasible set ``\mathcal{X}_t`` is itself an + optimization-level object — possibly a full nonconvex program, as in + the hydrothermal case study. Any learned policy must produce + decisions that *satisfy it exactly*, not approximately. + +## The dynamic-programming recursion + +Under the Markovian assumption that ``(x_{t-1}, w_t)`` summarizes the +history (stagewise-independent ``w_t``, or an augmented state otherwise), +the problem admits Bellman's recursion. Define the **cost-to-go** +(or *value*) **function** at the end of stage ``t``: + +```math +V_t(x_{t-1}, w_t) \;=\; +\min_{(u_t, x_t) \in \mathcal{X}_t(x_{t-1}, w_t)} +\; c_t(x_t, u_t) + \mathbb{E}_{w_{t+1}}\bigl[ V_{t+1}(x_t, w_{t+1}) \bigr], +``` + +with ``V_{T+1} \equiv 0``. The optimal policy acts greedily against the +expected cost-to-go: at each stage it trades the immediate cost +``c_t`` against the expected future cost +``\mathbb{E}[V_{t+1}(x_t, \cdot)]`` of the state it leaves behind. In +resource-storage problems this expected cost-to-go *is* the "value of +water" (or of inventory): its negative gradient with respect to the stored +quantity is the marginal price at which storing beats releasing. + +The recursion is conceptually complete and computationally hopeless in +general: ``V_t`` is a function on the full state space, and any grid-based +representation grows exponentially with the state dimension — Bellman's +*curse of dimensionality*. Every practical method is a way of +approximating either the value function or the policy. + +## Solution families + +Three broad families dominate practice; the third is the one this package +implements. + +### Scenario trees and the deterministic equivalent + +Discretize the uncertainty into a finite **scenario tree** and attach one +copy of the decision variables to every node. The result is a single — +typically enormous — mathematical program, the **deterministic +equivalent** (DE), whose solution is exact *for the tree*. The tree grows +exponentially in ``T``, so pure scenario-tree methods are confined to +short horizons or coarse discretizations. The DE returns in +[Three training formulations](@ref) in a different role — not as a +solution method but as a *differentiable training oracle* for a policy, +evaluated one sampled trajectory at a time, which sidesteps the +exponential growth entirely. + +### Value-function approximation: SDDP + +**Stochastic dual dynamic programming** (SDDP) exploits convexity: when +each stage problem is convex in ``x_{t-1}``, the cost-to-go +``\mathbb{E}[V_{t+1}]`` is convex and can be outer-approximated by +supporting hyperplanes ("cuts") generated from stage duals. SDDP is the +workhorse of long-horizon planning under uncertainty and the baseline the +case studies compare against; +[Stochastic dual dynamic programming](@ref) develops it in detail — +including what must be done, and what is silently given up, when the true +stage problem is *nonconvex*. + +### [Decision rules](@id decision-rules-sec) + +The third family approximates the **policy** directly: restrict ``\Pi`` to +a parametric class + +```math +u_t = \pi_\theta\bigl(w_{1:t}, x_{t-1}\bigr), \qquad \theta \in \Theta, +``` + +and optimize over the finite-dimensional parameter ``\theta`` instead of +over the space of all measurable policies. Nonanticipativity holds *by +construction* — the rule only ever reads the history. The classical +instance is the **linear decision rule** (LDR/affine policy), where +``\pi_\theta`` is affine in the observations: tractable, sometimes +provably near-optimal, but limited in expressiveness. Replacing the affine +map with a deep network gives a **deep decision rule** with the opposite +profile: expressive, but raising two difficulties that the naive +"learn a network that outputs actions" approach does not survive in +constrained physical systems: + +1. **Feasibility.** A network output has no reason to satisfy + ``\mathcal{X}_t`` — and in operations, constraint violation is not a + soft error. Penalizing violations reintroduces exactly the kind of + hyperparameter tuning decision rules were supposed to avoid. +2. **Gradient signal.** If feasibility is enforced by an optimization + layer, training requires differentiating through a solver — expensive + and fragile if done by unrolling or generic implicit differentiation at + scale. + +[The TS-DDR framework](@ref) resolves both at +once: the network outputs *target states* rather than actions, a +projection subproblem restores feasibility exactly, and Lagrangian duality +supplies the training gradient at the cost of the solve itself. The +[strict variant](@ref "Strict mode: penalty-free gradient signal") +sharpens this further when targets can be guaranteed reachable by +construction. + +## What "solving" means: bounds and simulation + +Because all practical methods approximate, empirical comparisons rest on +two complementary quantities, used throughout the case studies: + +- A **lower bound** (for minimization): SDDP's cut model provides a valid + lower bound on the expected cost *of the problem its cuts actually + model*. When the cut model is a convex relaxation of a nonconvex stage + problem, + the bound is a bound on the *relaxed* problem — an important subtlety + developed in [The bound and the forward cost](@ref). +- A **simulation (forward) cost**: the expected cost of a concrete policy, + estimated by rolling it out on the *true* stage problems over sampled + scenarios. This is the only number that treats every method — cuts, + linear rules, deep rules — on identical footing, and it is the primary + metric of the case studies (see the + [paired evaluation protocol](@ref "Paired evaluation protocol") used in + the hydrothermal study). + +The gap between the two jointly measures the suboptimality of the policy +*and* the fidelity of the model used to bound it — and keeping those two +contributions separate is a recurring theme, made precise for SDDP in +[The bound and the forward cost](@ref). + +## Further reading + +- Shapiro, Dentcheva, Ruszczyński, *Lectures on Stochastic Programming* + (SIAM) — the standard reference for the general theory. +- Bertsekas, *Dynamic Programming and Optimal Control* — the + control-theoretic view of the same recursion. +- Ben-Tal et al., *Adjustable robust solutions of uncertain linear + programs* (2004) — the origin of affine decision rules. +- Rosemberg, Street, Valladão, Van Hentenryck, + [*Efficiently Training Deep-Learning Parametric Policies using + Lagrangian Duality*](https://arxiv.org/abs/2405.14973) — the TS-DDR + paper this package implements. diff --git a/docs/src/theory/sddp.md b/docs/src/theory/sddp.md new file mode 100644 index 0000000..6b0f109 --- /dev/null +++ b/docs/src/theory/sddp.md @@ -0,0 +1,185 @@ +# Stochastic dual dynamic programming + +```@meta +CurrentModule = DecisionRules +``` + +Stochastic dual dynamic programming (SDDP) is the industrial standard for +long-horizon planning under uncertainty and the baseline against which the +case studies are evaluated — a fair comparison requires both methods at +the same depth. Its guarantees rest on one structural assumption, +convexity of the stage problem in the state; making that assumption +precise leads directly to the **inconsistent-formulation** variant used +when the true stage problem is nonconvex, and to the +*bound-versus-forward gap*, the quantity that measures what the +convexification gives up. (DecisionRules.jl does not implement SDDP; the +baselines use [SDDP.jl](https://github.com/odow/SDDP.jl).) + +## Cutting-plane approximation of the cost-to-go + +Recall the dynamic-programming recursion of +[Multistage stochastic optimization](@ref): the optimal stage decision +trades immediate cost against the expected cost-to-go +``\mathcal{V}_{t+1}(x_t) := \mathbb{E}_{w_{t+1}}[V_{t+1}(x_t, w_{t+1})]``. +SDDP (Pereira & Pinto, 1991) replaces ``\mathcal{V}_{t+1}`` by a +polyhedral outer approximation built from **cuts**, + +```math +\mathcal{V}_{t+1}(x) \;\ge\; \underline{\mathcal{V}}_{t+1}(x) +\;=\; \max_{k = 1, \ldots, K} \;\alpha_k + \langle \beta_k,\, x \rangle , +``` + +and iterates two passes over a sampled scenario lattice: + +- **Forward pass.** Sample a trajectory ``w_{1:T}``; solve the stage + problems in sequence with ``\underline{\mathcal{V}}_{t+1}`` in place of + the true cost-to-go, recording the visited states ``x_t``. The + accumulated stage costs of many forward passes estimate the expected + cost of the *current cut policy* — an upper-bound estimator (in + expectation) for minimization. +- **Backward pass.** At each visited state ``x_t``, re-solve the + stage-``(t{+}1)`` problems for every uncertainty realization, and read + the **dual multipliers** of the constraints through which ``x_t`` + enters (the state-coupling rows). Averaging over realizations + yields a subgradient ``\beta`` of ``\underline{\mathcal{V}}_{t+1}`` at + ``x_t`` and an intercept ``\alpha`` — a new cut, appended to the model. + +The value of the first-stage problem under the current cuts is a valid +**lower bound** on the optimal expected cost, monotonically nondecreasing +as cuts accumulate. Under standard assumptions (finite support, +stagewise independence, relatively complete recourse), the bound and the +forward-cost estimate converge to the common optimal value. + +## Where convexity enters + +Every step above leans on convexity of the stage problem in the incoming +state ``x_{t-1}``: + +1. **Cut validity.** A cut is a supporting hyperplane; it under-estimates + ``\mathcal{V}_{t+1}`` everywhere only if ``\mathcal{V}_{t+1}`` is + convex. Convexity of ``V_{t+1}(\cdot, w)`` in the state follows from + convexity of the stage feasible set and cost — and is *inherited + backwards* through the recursion. +2. **Dual attainment.** The subgradient ``\beta`` is a Lagrange + multiplier; strong duality (no duality gap) is what makes the + multiplier a subgradient of the value function rather than merely a + local sensitivity. + +If the stage problem is **nonconvex** in the state, both properties +fail: duals of a nonconvex solve are local objects, and a "cut" built +from them can *cut off* the true value function. SDDP as stated simply +does not apply. + +## Inconsistent formulations: convex cuts, nonconvex stage problems + +The pragmatic and widely used response is to run the two passes on +**different formulations** of the same stage: + +- the **backward pass** (cut generation) uses a **convex relaxation** + ``\mathcal{X}_t^{\mathrm{rel}} \supseteq \mathcal{X}_t`` of the stage + feasible set; +- the **forward pass** (state sampling and policy simulation) uses the + **true nonconvex stage problem** ``\mathcal{X}_t``. + +We refer to this as SDDP with **inconsistent formulations**. It is +well defined: the relaxed stage problem is convex in the state, so the +cuts are valid *for the relaxed problem*, and the recursion converges on +that surrogate. The forward pass then evaluates the resulting +value-function approximation against the stage problem that will +actually be operated. Concretely, the operating policy is + +```math +u_t^{\mathrm{SDDP}}(x_{t-1}, w_t) \;\in\; +\arg\min_{(u_t, x_t) \in \mathcal{X}_t(x_{t-1}, w_t)} +\; c_t(x_t, u_t) + \underline{\mathcal{V}}_{t+1}^{\mathrm{rel}}(x_t) : +``` + +true feasibility inside the stage, *relaxation-priced* future outside +it. + +### What the surrogate misprices + +The quality of this policy hinges on how well the relaxed cost-to-go +``\underline{\mathcal{V}}^{\mathrm{rel}}`` prices the *true* marginal +value of the state. Relaxation only widens the stage feasible set, so +the surrogate can realize transitions the true system cannot — it +systematically **underestimates the cost of future operation** wherever +the relaxation is loose, and therefore undervalues precisely the states +whose worth derives from relieving that future stress. Whether the +resulting error is negligible or material is a property of the +*instance and its operating regime*, not of the algorithm. The +[hydropower case study](@ref "The Bolivian interconnected system") +works through a concrete mechanism — a conic relaxation of the network +constraints mispricing stored energy — on an instance family chosen to +sit in the regime where the question is live. + +## The bound and the forward cost + +The inconsistent scheme produces two headline numbers with different +epistemic status: + +- ``\underline{z}^{\mathrm{rel}}`` — the converged **backward bound**: a + valid lower bound on the expected cost of the *relaxed* multistage + problem. Because relaxation only widens each stage's feasible set, it + is also a valid lower bound on the true problem — but a *slack* one: + it is attained (if at all) by relaxed trajectories that no feasible + policy can reproduce. +- ``\hat{z}`` — the **forward simulation cost**: the Monte Carlo + estimate of the expected cost of the actual operating policy on the + true stage problems. + +Their relative difference, + +```math +\mathrm{gap} \;=\; +\frac{\hat{z} - \underline{z}^{\mathrm{rel}}} + {\underline{z}^{\mathrm{rel}}}, +``` + +is the **bound-versus-forward gap**. It conflates two contributions that +cannot be separated without further work: ordinary SDDP suboptimality +(finitely many cuts) and the **cost of convexification** — the systematic +error of pricing the future on a relaxed model. On instances where the +relaxation is nearly tight, the gap collapses to the first contribution +and SDDP is close to unbeatable; as the relaxation loosens, the gap grows +and becomes *headroom*: expected cost that a method free of the +convexification assumption is, at least in principle, able to recover. +TS-DDR trains directly on the nonconvex stage problems — its gradient +comes from duals of the *true* stage solves, not from a relaxation — so +the gap is the natural ex-ante measure of how much room such a method has +on a given instance. The hydropower case study reports this gap +explicitly for its instance family. + +Two disciplines keep the comparison honest, and both are enforced in the +case studies: + +1. **Bounds are horizon-specific.** A bound computed on a + ``T``-stage problem does not bound a ``T' < T``-stage simulation + metric; training and evaluation horizons must be stated and matched. +2. **Policies are compared on the forward metric only.** The only number + comparable across SDDP, TS-DDR, and any other method is the simulated + expected cost under identical stage problems and identical scenarios — + hence the paired-scenario protocol of the case studies. + +## Complementarity with decision rules + +SDDP and TS-DDR occupy dual corners of the design space. SDDP +approximates the *value function* and recovers actions by re-solving a +stage problem at operation time; its strength is a self-certifying bound +and decades of industrial hardening, and its structural commitment is +convexity of the stage model that generates cuts. TS-DDR approximates the +*policy* and needs no convexity — the projection subproblem may be an +arbitrary NLP — but it certifies nothing by itself: its quality is +established empirically, by simulation against a baseline. This is why +the case studies always report both: SDDP supplies the yardstick (a bound +and a strong incumbent policy), and the decision rule is measured against +it on the true stage problems. + +## Further reading + +- Pereira & Pinto, *Multi-stage stochastic optimization applied to energy + planning*, Mathematical Programming 52 (1991) — the original SDDP paper. +- Dowson & Kapelevich, *SDDP.jl: a Julia package for stochastic dual + dynamic programming*, INFORMS Journal on Computing 33 (2021). +- Shapiro, *Analysis of stochastic dual dynamic programming method*, + EJOR 209 (2011) — convergence analysis and statistical stopping. diff --git a/examples/HydroPowerModels/LocalPreferences.toml b/examples/HydroPowerModels/LocalPreferences.toml deleted file mode 100644 index a956f6c..0000000 --- a/examples/HydroPowerModels/LocalPreferences.toml +++ /dev/null @@ -1,6 +0,0 @@ -# Use pip instead of uv as the pip backend for CondaPkg -# This is needed because uv requires libstdcxx-ng >=13, which is not available on this HPC system -# The environment variable JULIA_CONDAPKG_PIP_BACKEND=pip also works - -[CondaPkg] -pip_backend = "pip" diff --git a/examples/HydroPowerModels/Project.toml b/examples/HydroPowerModels/Project.toml deleted file mode 100644 index 135292b..0000000 --- a/examples/HydroPowerModels/Project.toml +++ /dev/null @@ -1,44 +0,0 @@ -[deps] -CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" -CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" -CUDSS = "45b445bb-4962-46a0-9369-b4df9d0f772e" -DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" -Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" -DecisionRules = "47937410-f832-486f-8300-12c95b225dfc" -DiffOpt = "930fe3bc-9c6b-11ea-2d94-6184641e85e7" -Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" -Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9" -JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" -JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" -JuMP = "4076af6c-e467-56ae-b986-b466b2749572" -KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c" -Logging = "56ddb016-857b-54e1-b83d-db4d58db5568" -MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6" -MadNLPGPU = "d72a61cc-809d-412f-99be-fd81f4b8a598" -Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" -Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" -Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" -Wandb = "ad70616a-06c9-5745-b1f1-6a5f42545108" - -[sources] -Ipopt = {rev = "master", url = "https://github.com/jump-dev/Ipopt.jl.git"} -MadNLP = {rev = "master", url = "https://github.com/MadNLP/MadNLP.jl.git"} -MadNLPGPU = {rev = "master", subdir = "lib/MadNLPGPU", url = "https://github.com/MadNLP/MadNLP.jl.git"} - -[compat] -CSV = "0.10" -CUDA = "6" -CUDSS = "0.7, 0.8" -DiffOpt = "0.5.6, 0.6" -Flux = "0.16" -Ipopt = "1, 2" -JLD2 = "0.6" -JSON = "1" -JuMP = "1" -KernelAbstractions = "0.9" -MadNLP = "0.8, 0.9, 0.10" -MadNLPGPU = "0.7, 0.8, 0.9, 0.10" -Tables = "1" -Wandb = "0.5" -julia = "1.11, 1.12" diff --git a/examples/HydroPowerModels/README.md b/examples/HydroPowerModels/README.md deleted file mode 100644 index 7a6d62b..0000000 --- a/examples/HydroPowerModels/README.md +++ /dev/null @@ -1,148 +0,0 @@ -# HydroPowerModels — Long-Term Hydrothermal Dispatching (Bolivia LTHD) - -This directory contains the primary application from the paper: training -Two-Stage Deep Decision Rules (TS-DDR) for the Bolivia Long-Term -Hydrothermal Dispatching problem with 10 hydro units, 96 monthly stages, -and AC/SOC/DC power-flow formulations. - -## Problem overview - -The Bolivia LTHD problem couples hydro reservoir dynamics (water balance) -with a power network dispatch (OPF) at each stage. Stochastic inflows -drive reservoir levels; the decision rule maps (inflow history, current -state) to reservoir-level targets, and an NLP optimizer dispatches -generation to meet those targets at minimum cost. - -## Training scripts - -### TS-DDR (Deep Decision Rules — LSTM policy) - -| Script | Decomposition | Reference | -|--------|--------------|-----------| -| `train_dr_hydropowermodels.jl` | Deterministic equivalent (GPU-enabled) | Extension §1 | -| `train_dr_hydropowermodels_subproblems.jl` | Stage-wise (single shooting) | Extension §2 | -| `train_dr_hydropowermodels_multipleshooting.jl` | Windowed (multiple shooting) | Extension §3 | - -These use a `StateConditionedPolicy` (LSTM encoder + state-conditioned dense -layers, `[128, 128]`, sigmoid activation). - -### TS-LDR (Linear Decision Rules — linear policy) - -| Script | Decomposition | Reference | -|--------|--------------|-----------| -| `train_ldr_hydropowermodels.jl` | Deterministic equivalent (GPU-enabled) | §3 | - -TS-LDR uses `dense_multilayer_nn` with identity activation — a composition -of linear layers that is equivalent to a single linear map from -(uncertainties, state) to targets. Same training pipeline as TS-DDR; the -only difference is the policy architecture. - -All training scripts share the data loader (`load_hydropowermodels.jl`), -log to Weights & Biases, and save the best model to JLD2. - -### GPU training - -`train_dr_hydropowermodels.jl` auto-detects CUDA and switches to -MadNLP+CUDSS on GPU when available. Submit via: - -```bash -cd examples/HydroPowerModels -mkdir -p logs -sbatch run_train_deteq_gpu.sbatch -``` - -### Penalty schedule - -All training scripts support `:default_annealed` penalty schedules that -gradually increase target-violation penalties during training, improving -convergence on the nonconvex AC formulation. - -### Rollout metrics - -For deterministic-equivalent training, `metrics/loss` is computed on the same -target-state history produced by the policy. The matching held-out metric is -`metrics/rollout_objective_no_deficit`, which now uses `RolloutEvaluation(...; -policy_state=:target)` in `train_dr_hydropowermodels.jl`. - -The same script also logs -`metrics/rollout_realized_objective_no_deficit` with `policy_state=:realized`. -That is the closed-loop deployment diagnostic: each stage passes the optimizer's -realized reservoir state back to the policy. It can be harder than the target-state -metric, especially while the policy is trained through the deterministic equivalent. - -All rollout objective metrics exclude the target-slack/deficit penalty term. Track -the paired target-violation share and `metrics/target_penalty_multiplier` to see -whether a low operational objective is coming from feasible targets or from the -policy relying on slack. - -## Evaluation and baselines - -| Script | Purpose | -|--------|---------| -| `evaluate_hydro_policies.jl` | Load all trained TS-DDR and TS-LDR models and evaluate on a common out-of-sample scenario set using stage-wise ACP rollout; writes `eval_costs.csv` | -| `eval_jump_de.jl` | Solve the DE with a constant policy and save a reference solution (JLD2) for cross-validation with ExaModels | -| `check_consistent_state_paths.jl` | Verify that stage-wise, deterministic equivalent, and multiple-shooting decompositions produce identical state trajectories under the same policy and inflows | - -## SDDP baselines - -These scripts use a dedicated Julia environment in `sddp/`. The inconsistent -SOC-backward/AC-forward baseline uses -[HydroPowerModels.jl](https://github.com/LAMPSPUC/HydroPowerModels.jl), SDDP.jl, -Clarabel for the SOC backward pass, and MadNLP for the AC forward pass. Training -runs log iteration and final simulation metrics to Weights & Biases using the -same keys as the DR runs: `metrics/loss` is the SDDP bound, and -`metrics/rollout_realized_objective_no_deficit` is the SDDP forward-pass -objective. SDDP iterations are logged as `batch` so W&B plots can share the same -x-axis as the DR training runs. Because SDDP solves the forward policy -stage-wise, that forward-pass objective is already the no-target-penalty -objective. - -| Script | Description | -|--------|-------------| -| `sddp/run_sddp.jl` | Train SDDP with a consistent convex (SOCWRConic) formulation | -| `sddp/run_sddp_inconsistent.jl` | Train SDDP with SOCWRConic backward pass and ACP forward pass | -| `sddp/run_sddp_inconsistent.sbatch` | Submit the SOC-backward/AC-forward run with a 12-hour wall time | -| `sddp/simulate_sddp_policy.jl` | Simulate a pre-trained SDDP policy under ACP and produce comparison plots | - -## Learning-to-Optimize (L2O) pipeline - -| Script | Description | -|--------|-------------| -| `gen_inputs_l2O_hydropowermodels.jl` | Generate input datasets for the L2O supervised pipeline (requires [L2O.jl](https://github.com/andrewrosemberg/L2O.jl)) | -| `train_dr_l2O_supervised.jl` | Supervised pre-training of a decision rule from L2O-generated optimal solutions | - -## Subproblem export (generating `.mof.json` files) - -The training pipeline (`load_hydropowermodels.jl`) reads pre-exported `.mof.json` -subproblem templates rather than depending on HydroPowerModels.jl at training time. -These files already ship with the repository: - -``` -bolivia/ACPPowerModel.mof.json -bolivia/SOCWRConicPowerModel.mof.json -bolivia/DCPPowerModel.mof.json -case3/ACPPowerModel.mof.json -``` - -To regenerate them (e.g. after updating HydroPowerModels data or adding a new -formulation), use `export_subproblem_mof.jl`: - -```bash -julia export_subproblem_mof.jl bolivia ACPPowerModel -julia export_subproblem_mof.jl bolivia SOCWRConicPowerModel -``` - -This builds the full SDDP model via HydroPowerModels.jl, extracts one stage's -subproblem from the policy graph, removes the unnamed slack variable that -HydroPowerModels adds, and writes a clean JuMP `.mof.json` to disk. Requires -HydroPowerModels.jl and a solver (Mosek by default). - -## Data - -- `bolivia/` — Bolivia case: `hydro.json` (10 hydro units), `inflows.csv` (historical scenarios), `ACPPowerModel.mof.json` / `SOCWRConicPowerModel.mof.json` / `DCPPowerModel.mof.json` (subproblem templates) -- `case3/` — Small 3-bus test case for development - -## Dependencies - -See `Project.toml` in this directory. Key packages: DecisionRules, DiffOpt, -Ipopt+HSL, MadNLP+MadNLPGPU+CUDA (GPU), Flux, JuMP, Wandb. diff --git a/examples/HydroPowerModels/bolivia/ACPPowerModel.mof.json b/examples/HydroPowerModels/bolivia/ACPPowerModel.mof.json deleted file mode 100644 index 5e63547..0000000 --- a/examples/HydroPowerModels/bolivia/ACPPowerModel.mof.json +++ /dev/null @@ -1,39045 +0,0 @@ -{ - "name": "MathOptFormat Model", - "version": { - "major": 1, - "minor": 7 - }, - "variables": [ - { - "name": "reservoir[1]_in", - "primal_start": 0.0 - }, - { - "name": "reservoir[2]_in", - "primal_start": 0.0 - }, - { - "name": "reservoir[3]_in", - "primal_start": 0.0 - }, - { - "name": "reservoir[4]_in", - "primal_start": 0.0 - }, - { - "name": "reservoir[5]_in", - "primal_start": 0.0 - }, - { - "name": "reservoir[6]_in", - "primal_start": 0.0 - }, - { - "name": "reservoir[7]_in", - "primal_start": 0.0 - }, - { - "name": "reservoir[8]_in", - "primal_start": 0.0 - }, - { - "name": "reservoir[9]_in", - 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"load_bus": 21, - "status": 1, - "qd": 0, - "pd": 0, - "index": 21 - }, - "22": { - "load_bus": 22, - "status": 1, - "qd": 0.024149634097409733, - "pd": 0.2012469508117478, - "index": 22 - }, - "23": { - "load_bus": 23, - "status": 1, - "qd": 0.00009651327732773278, - "pd": 0.0008042773110644398, - "index": 23 - }, - "24": { - "load_bus": 24, - "status": 1, - "qd": 0.0036080868952609543, - "pd": 0.030067390793841287, - "index": 24 - }, - "25": { - "load_bus": 25, - "status": 1, - "qd": 0.00009651327732773278, - "pd": 0.0008042773110644398, - "index": 25 - }, - "26": { - "load_bus": 26, - "status": 1, - "qd": 0.03401845659565956, - "pd": 0.283487138297163, - "index": 26 - } - } -} \ No newline at end of file diff --git a/examples/HydroPowerModels/bolivia/SOCWRConicPowerModel.mof.json b/examples/HydroPowerModels/bolivia/SOCWRConicPowerModel.mof.json deleted file mode 100644 index 14d2ff7..0000000 --- a/examples/HydroPowerModels/bolivia/SOCWRConicPowerModel.mof.json +++ /dev/null @@ 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"set": { - "type": "LessThan", - "upper": 100.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(2, 3, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(3, 1, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(1, 1, 3)]" - }, - "set": { - "type": "LessThan", - "upper": 1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(2, 2, 3)]" - }, - "set": { - "type": "LessThan", - "upper": 0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(3, 2, 1)]" - }, - "set": { - "type": "LessThan", - "upper": 0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(1, 3, 1)]" - }, - "set": { - "type": "LessThan", - "upper": 1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(2, 3, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(3, 1, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(1, 1, 3)]" - }, - "set": { - "type": "LessThan", - "upper": 1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(2, 2, 3)]" - }, - "set": { - "type": "LessThan", - "upper": 0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(3, 2, 1)]" - }, - "set": { - "type": "LessThan", - "upper": 0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(1, 3, 1)]" - }, - "set": { - "type": "LessThan", - "upper": 1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "reservoir[1]_out" - }, - "set": { - "type": "LessThan", - "upper": 0.54 - } - }, - { - "function": { - "type": "Variable", - "name": "outflow[1]" - }, - "set": { - "type": "LessThan", - "upper": 80.0 - } - } - ] -} diff --git a/examples/HydroPowerModels/case3/ACPPowerModel_det_equivalent.mof.json b/examples/HydroPowerModels/case3/ACPPowerModel_det_equivalent.mof.json deleted file mode 100644 index b93bb87..0000000 --- a/examples/HydroPowerModels/case3/ACPPowerModel_det_equivalent.mof.json +++ /dev/null @@ -1,57057 +0,0 @@ -{ - "name": "MathOptFormat Model", - "version": { - "major": 1, - "minor": 7 - }, - "variables": [ - { - "name": "0_vm[2]#1" - }, - { - "name": "0_vm[3]#1" - }, - { - "name": "0_vm[1]#1" - }, - { - "name": "0_pg[2]#1" - }, - { - "name": "0_pg[3]#1" - }, - { - "name": "0_pg[1]#1" - }, - { - "name": "0_qg[2]#1" - }, - { - "name": "0_qg[3]#1" - }, - { - "name": "0_qg[1]#1" - }, - { - "name": "0_p[(2, 3, 2)]#1" - }, - { - "name": "0_p[(3, 1, 2)]#1" - }, - { - "name": "0_p[(1, 1, 3)]#1" - }, - { - "name": "0_p[(2, 2, 3)]#1" - }, - { - "name": "0_p[(3, 2, 1)]#1" - }, - { - "name": "0_p[(1, 3, 1)]#1" - }, - { - "name": "0_q[(2, 3, 2)]#1" - }, - { - "name": "0_q[(3, 1, 2)]#1" - }, - { - "name": "0_q[(1, 1, 3)]#1" - }, - { - "name": "0_q[(2, 2, 3)]#1" - }, - { - 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0.0, - 0.0, - 0.0, - 0.0 - ] - }, - "set": { - "type": "RotatedSecondOrderCone", - "dimension": 4 - } - }, - { - "name": "c2_4", - "function": { - "type": "VectorAffineFunction", - "terms": [ - { - "output_index": 1, - "scalar_term": { - "coefficient": 0.7071067811865475, - "variable": "0_w[1]" - } - }, - { - "output_index": 2, - "scalar_term": { - "coefficient": 0.7071067811865475, - "variable": "0_w[2]" - } - }, - { - "output_index": 3, - "scalar_term": { - "coefficient": 1.0, - "variable": "0_wr[(1, 2)]" - } - }, - { - "output_index": 4, - "scalar_term": { - "coefficient": 1.0, - "variable": "0_wi[(1, 2)]" - } - } - ], - "constants": [ - 0.0, - 0.0, - 0.0, - 0.0 - ] - }, - "set": { - "type": "RotatedSecondOrderCone", - "dimension": 4 - } - }, - { - "name": "c3_4", - "function": { - "type": "VectorAffineFunction", - "terms": [ - { - "output_index": 1, - "scalar_term": { - "coefficient": 0.7071067811865475, - "variable": "0_w[1]" - } - }, - { - "output_index": 2, - "scalar_term": { - "coefficient": 0.7071067811865475, - "variable": "0_w[3]" - } - }, - { - "output_index": 3, - "scalar_term": { - "coefficient": 1.0, - "variable": "0_wr[(1, 3)]" - } - }, - { - "output_index": 4, - "scalar_term": { - "coefficient": 1.0, - "variable": "0_wi[(1, 3)]" - } - } - ], - "constants": [ - 0.0, - 0.0, - 0.0, - 0.0 - ] - }, - "set": { - "type": "RotatedSecondOrderCone", - "dimension": 4 - } - }, - { - "function": { - "type": "Variable", - "name": "reservoir[1]_in" - }, - "set": { - "type": "EqualTo", - "value": 0.18 - } - }, - { - "function": { - "type": "Variable", - "name": "inflow[1]" - }, - "set": { - "type": "EqualTo", - "value": 120.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_w[2]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.81 - } - }, - { - "function": { - "type": "Variable", - "name": "0_w[3]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.81 - } - }, - { - "function": { - "type": "Variable", - "name": "0_w[1]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.81 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wr[(3, 2)]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.404998282210762 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wr[(1, 2)]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.404998282210762 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wr[(1, 3)]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.404998282210762 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wi[(3, 2)]" - }, - "set": { - "type": "GreaterThan", - "lower": -1.0478922201020873 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wi[(1, 2)]" - }, - "set": { - "type": "GreaterThan", - "lower": -1.0478922201020873 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wi[(1, 3)]" - }, - "set": { - "type": "GreaterThan", - "lower": -1.0478922201020873 - } - }, - { - "function": { - "type": "Variable", - "name": "0_pg[2]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_pg[3]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_pg[1]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_qg[2]" - }, - "set": { - "type": "GreaterThan", - "lower": -100.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_qg[3]" - }, - "set": { - "type": "GreaterThan", - "lower": -100.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_qg[1]" - }, - "set": { - "type": "GreaterThan", - "lower": -100.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(2, 3, 2)]" - }, - "set": { - "type": "GreaterThan", - "lower": -0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(3, 1, 2)]" - }, - "set": { - "type": "GreaterThan", - "lower": -0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(1, 1, 3)]" - }, - "set": { - "type": "GreaterThan", - "lower": -1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(2, 2, 3)]" - }, - "set": { - "type": "GreaterThan", - "lower": -0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(3, 2, 1)]" - }, - "set": { - "type": "GreaterThan", - "lower": -0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(1, 3, 1)]" - }, - "set": { - "type": "GreaterThan", - "lower": -1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(2, 3, 2)]" - }, - "set": { - "type": "GreaterThan", - "lower": -0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(3, 1, 2)]" - }, - "set": { - "type": "GreaterThan", - "lower": -0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(1, 1, 3)]" - }, - "set": { - "type": "GreaterThan", - "lower": -1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(2, 2, 3)]" - }, - "set": { - "type": "GreaterThan", - "lower": -0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(3, 2, 1)]" - }, - "set": { - "type": "GreaterThan", - "lower": -0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(1, 3, 1)]" - }, - "set": { - "type": "GreaterThan", - "lower": -1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "reservoir[1]_out" - }, - "set": { - "type": "GreaterThan", - "lower": 0.0 - } - }, - { - "function": { - "type": "Variable", - "name": "min_volume_violation[1]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.0 - } - }, - { - "function": { - "type": "Variable", - "name": "outflow[1]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.0 - } - }, - { - "function": { - "type": "Variable", - "name": "spill[1]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.0 - } - }, - { - "function": { - "type": "Variable", - "name": "min_outflow_violation[1]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.0 - } - }, - { - "function": { - "type": "Variable", - "name": "deficit[1]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.0 - } - }, - { - "function": { - "type": "Variable", - "name": "deficit[2]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.0 - } - }, - { - "function": { - "type": "Variable", - "name": "deficit[3]" - }, - "set": { - "type": "GreaterThan", - "lower": 0.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_w[2]" - }, - "set": { - "type": "LessThan", - "upper": 1.2100000000000002 - } - }, - { - "function": { - "type": "Variable", - "name": "0_w[3]" - }, - "set": { - "type": "LessThan", - "upper": 1.2100000000000002 - } - }, - { - "function": { - "type": "Variable", - "name": "0_w[1]" - }, - "set": { - "type": "LessThan", - "upper": 1.2100000000000002 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wr[(3, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 1.2100000000000002 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wr[(1, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 1.2100000000000002 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wr[(1, 3)]" - }, - "set": { - "type": "LessThan", - "upper": 1.2100000000000002 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wi[(3, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 1.0478922201020873 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wi[(1, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 1.0478922201020873 - } - }, - { - "function": { - "type": "Variable", - "name": "0_wi[(1, 3)]" - }, - "set": { - "type": "LessThan", - "upper": 1.0478922201020873 - } - }, - { - "function": { - "type": "Variable", - "name": "0_pg[2]" - }, - "set": { - "type": "LessThan", - "upper": 0.5 - } - }, - { - "function": { - "type": "Variable", - "name": "0_pg[3]" - }, - "set": { - "type": "LessThan", - "upper": 0.8 - } - }, - { - "function": { - "type": "Variable", - "name": "0_pg[1]" - }, - "set": { - "type": "LessThan", - "upper": 1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_qg[2]" - }, - "set": { - "type": "LessThan", - "upper": 100.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_qg[3]" - }, - "set": { - "type": "LessThan", - "upper": 100.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_qg[1]" - }, - "set": { - "type": "LessThan", - "upper": 100.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(2, 3, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(3, 1, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(1, 1, 3)]" - }, - "set": { - "type": "LessThan", - "upper": 1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(2, 2, 3)]" - }, - "set": { - "type": "LessThan", - "upper": 0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(3, 2, 1)]" - }, - "set": { - "type": "LessThan", - "upper": 0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_p[(1, 3, 1)]" - }, - "set": { - "type": "LessThan", - "upper": 1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(2, 3, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(3, 1, 2)]" - }, - "set": { - "type": "LessThan", - "upper": 0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(1, 1, 3)]" - }, - "set": { - "type": "LessThan", - "upper": 1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(2, 2, 3)]" - }, - "set": { - "type": "LessThan", - "upper": 0.65 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(3, 2, 1)]" - }, - "set": { - "type": "LessThan", - "upper": 0.25 - } - }, - { - "function": { - "type": "Variable", - "name": "0_q[(1, 3, 1)]" - }, - "set": { - "type": "LessThan", - "upper": 1.0 - } - }, - { - "function": { - "type": "Variable", - "name": "reservoir[1]_out" - }, - "set": { - "type": "LessThan", - "upper": 0.54 - } - }, - { - "function": { - "type": "Variable", - "name": "outflow[1]" - }, - "set": { - "type": "LessThan", - "upper": 80.0 - } - } - ] -} diff --git a/examples/HydroPowerModels/case3/hydro.json b/examples/HydroPowerModels/case3/hydro.json deleted file mode 100644 index b906b22..0000000 --- a/examples/HydroPowerModels/case3/hydro.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "Hydrogenerators":[ - { - "index": 1, - "index_grid": 3, - "max_volume":0.54, - "min_volume":0, - "max_turn": 80, - "min_turn": 0, - "initial_volume":0.18, - "final_volume":0.0, - "production_factor":1, - "spill_cost":0, - "minimal_outflow_violation_cost":0, - "minimal_volume_violation_cost":0, - "downstream_turn": [], - "downstream_spill": [] - } - ], - "stage_hours": 1 -} diff --git a/examples/HydroPowerModels/case3/inflows.csv b/examples/HydroPowerModels/case3/inflows.csv deleted file mode 100644 index 1d09924..0000000 --- a/examples/HydroPowerModels/case3/inflows.csv +++ /dev/null @@ -1,12 +0,0 @@ -120,80,40 -105,70,35 -90,60,30 -75,50,25 -60,40,20 -45,30,15 -30,20,10 -45,30,15 -60,40,20 -75,50,25 -90,60,30 -105,70,35 diff --git a/examples/HydroPowerModels/case3/scenarioprobability.csv b/examples/HydroPowerModels/case3/scenarioprobability.csv deleted file mode 100644 index 24b1984..0000000 --- a/examples/HydroPowerModels/case3/scenarioprobability.csv +++ /dev/null @@ -1,12 +0,0 @@ -0.3,0.4,0.3 -0.3,0.4,0.3 -0.3,0.4,0.3 -0.3,0.4,0.3 -0.3,0.4,0.3 -0.3,0.4,0.3 -0.3,0.4,0.3 -0.3,0.4,0.3 -0.3,0.4,0.3 -0.3,0.4,0.3 -0.3,0.4,0.3 -0.3,0.4,0.3 diff --git a/examples/HydroPowerModels/check_consistent_state_paths.jl b/examples/HydroPowerModels/check_consistent_state_paths.jl deleted file mode 100644 index 349573a..0000000 --- a/examples/HydroPowerModels/check_consistent_state_paths.jl +++ /dev/null @@ -1,161 +0,0 @@ -using DecisionRules -using Random -using JuMP -using DiffOpt -using Ipopt - -HydroPowerModels_dir = dirname(@__FILE__) -include(joinpath(HydroPowerModels_dir, "load_hydropowermodels.jl")) - -# ---- Configuration ---- -case_name = "bolivia" # case3, bolivia -formulation = "ACPPowerModel" # DCPPowerModel, SOCWRConicPowerModel, ACPPowerModel -num_stages = 96 -window_size = 12 - -ipopt_attrs = optimizer_with_attributes( - Ipopt.Optimizer, - "print_level" => 0, - "linear_solver" => "mumps", -) - -diff_optimizer = () -> DiffOpt.diff_optimizer(ipopt_attrs) -diff_model = () -> DiffOpt.nonlinear_diff_model(ipopt_attrs) - -det_optimizer = optimizer_with_attributes(Ipopt.Optimizer, "print_level" => 0) - -function build_problem() - return build_hydropowermodels( - joinpath(HydroPowerModels_dir, case_name), - formulation * ".mof.json"; - num_stages=num_stages, - optimizer=diff_optimizer, - ) -end - -# ---- Stage-wise simulation ---- -sub_s, state_in_s, state_out_s, uncert_s, initial_state, max_volume = build_problem() -num_uncertainties = length(uncert_s[1]) - -# Constant policy so targets do not depend on state or uncertainty. -const_target = Float32.(max_volume) * 0.6 -policy(_) = const_target - -Random.seed!(1234) -base_sample = DecisionRules.sample(uncert_s) -base_values = [[u[2] for u in stage_u] for stage_u in base_sample] -uncertainties_s = [ - [(stage_u[i][1], base_values[t][i]) for i in eachindex(stage_u)] for - (t, stage_u) in enumerate(uncert_s) -] - -obj_stage = DecisionRules.simulate_multistage( - sub_s, state_in_s, state_out_s, initial_state, uncertainties_s, policy -) - -states_stage = Vector{Vector{Float64}}(undef, num_stages + 1) -states_stage[1] = initial_state -for t in 1:num_stages - states_stage[t + 1] = [value(pair[2]) for pair in state_out_s[t]] -end - -# ---- Deterministic equivalent ---- -sub_d, state_in_d, state_out_d, uncert_d, initial_state_d, _ = build_problem() - -Det_model = JuMP.Model(det_optimizer) -Det_model, uncert_d = DecisionRules.deterministic_equivalent!( - Det_model, sub_d, state_in_d, state_out_d, Float64.(initial_state_d), uncert_d -) -uncertainties_d = [ - [(stage_u[i][1], base_values[t][i]) for i in eachindex(stage_u)] for - (t, stage_u) in enumerate(uncert_d) -] - -states_policy = DecisionRules.simulate_states(initial_state_d, uncertainties_d, policy) -obj_det = DecisionRules.simulate_multistage( - Det_model, state_in_d, state_out_d, uncertainties_d, states_policy -) - -states_det = Vector{Vector{Float64}}(undef, num_stages + 1) -states_det[1] = initial_state_d -for t in 1:num_stages - states_det[t + 1] = [value(pair[2]) for pair in state_out_d[t]] -end - -# ---- Multiple shooting ---- -sub_w, state_in_w, state_out_w, uncert_w, initial_state_w, _ = build_problem() - -windows = DecisionRules.setup_shooting_windows( - sub_w, - state_in_w, - state_out_w, - Float64.(initial_state_w), - uncert_w; - window_size=window_size, - model_factory=diff_model, -) - -uncertainties_w = [ - [(stage_u[i][1], base_values[t][i]) for i in eachindex(stage_u)] for - (t, stage_u) in enumerate(uncert_w) -] -uncertainties_vec = [[Float32(u[2]) for u in stage_u] for stage_u in uncertainties_w] - -obj_shoot = DecisionRules.simulate_multiple_shooting( - windows, policy, Float32.(initial_state_w), uncertainties_w, uncertainties_vec -) - -states_shoot = Vector{Vector{Float64}}() -push!(states_shoot, Float64.(initial_state_w)) -current_state = Float64.(initial_state_w) -for window in windows - global current_state - window_range = window.stage_range - window_uncertainties_vec = uncertainties_vec[window_range] - targets = DecisionRules.predict_window_targets( - policy, current_state, window_uncertainties_vec - ) - DecisionRules.set_window_uncertainties!(window, uncertainties_w) - DecisionRules.solve_window( - window.model, - window.state_in_params, - window.state_out_params, - current_state, - targets, - ) - - for local_t in 1:length(window_range) - push!(states_shoot, [value(pair[2]) for pair in window.state_out_params[local_t]]) - end - current_state = states_shoot[end] -end - -# ---- Diagnostics ---- -function max_state_diff(a, b) - return maximum(abs.(vcat([abs.(a[t] .- b[t]) for t in eachindex(a)]...))) -end - -@assert all( - all( - uncertainties_s[t][i][2] == base_values[t][i] for i in eachindex(uncertainties_s[t]) - ) for t in 1:num_stages -) - -@assert all( - all( - uncertainties_w[t][i][2] == base_values[t][i] for i in eachindex(uncertainties_w[t]) - ) for t in 1:num_stages -) - -@assert all( - all( - uncertainties_d[t][i][2] == base_values[t][i] for i in eachindex(uncertainties_d[t]) - ) for t in 1:num_stages -) - -println("objective(stage): ", obj_stage) -println("objective(det): ", obj_det) -println("objective(shoot): ", obj_shoot) - -println("max |stage - det| state diff: ", max_state_diff(states_stage, states_det)) -println("max |stage - shoot| state diff: ", max_state_diff(states_stage, states_shoot)) diff --git a/examples/HydroPowerModels/compare_hydro_results.jl b/examples/HydroPowerModels/compare_hydro_results.jl deleted file mode 100644 index efbf6a3..0000000 --- a/examples/HydroPowerModels/compare_hydro_results.jl +++ /dev/null @@ -1,143 +0,0 @@ -# compare_hydro_results.jl -# -# Pull training histories from W&B and generate comparison plots for the docs. -# Saves plots to ../../docs/src/assets/ and prints a summary table. -# -# Usage: -# julia --project compare_hydro_results.jl [--run-names name1,name2,name3] -# -# By default, uses the 3 most recent "running" or "finished" runs in the RL project -# that match the three training methods. - -using Plots -using Statistics - -import Wandb -const wb = Wandb.wandb -const PC = parentmodule(typeof(wb)) - -const DOCS_ASSETS = joinpath(@__DIR__, "..", "..", "docs", "src", "assets") -mkpath(DOCS_ASSETS) - -api = wb.Api() -all_runs = api.runs("RL", order="-created_at") - -methods_wanted = ["deterministic_equivalent", "subproblems", "multiple_shooting"] -method_labels = Dict( - "deterministic_equivalent" => "Deterministic Equivalent", - "subproblems" => "Stage-wise Subproblems", - "multiple_shooting" => "Multiple Shooting (w=12)", -) -method_colors = Dict( - "deterministic_equivalent" => :blue, - "subproblems" => :red, - "multiple_shooting" => :green, -) - -runs = Dict{String,Any}() -for i in 0:29 - try - r = all_runs[i] - method = PC.pyconvert(String, get(r.config, "training_method", "?")) - state = PC.pyconvert(String, r.state) - if method in methods_wanted && !haskey(runs, method) && state in ("running", "finished", "crashed") - runs[method] = r - end - catch - break - end - length(runs) == 3 && break -end - -println("Using runs:") -for (m, r) in runs - println(" $(method_labels[m]): $(PC.pyconvert(String, r.name)) ($(PC.pyconvert(String, r.state)))") -end - -function get_history(r, metric) - keys_list = PC.pylist([metric]) - hist = r.scan_history(keys=keys_list) - vals = Float64[] - for row in hist - v = try PC.pyconvert(Float64, get(row, metric, nothing)) catch; nothing end - !isnothing(v) && push!(vals, v) - end - return vals -end - -# ── Plot 1: Training convergence (in-sample loss) ──────────────────────────── - -plt1 = plot(; xlabel="Iteration", ylabel="Operational Cost (no deficit)", - title="Training Convergence", legend=:topright) -for m in methods_wanted - haskey(runs, m) || continue - vals = get_history(runs[m], "metrics/loss") - isempty(vals) && continue - plot!(plt1, 1:length(vals), vals; label=method_labels[m], color=method_colors[m], alpha=0.7) -end -savefig(plt1, joinpath(DOCS_ASSETS, "hydro_training_convergence.png")) -println("Saved hydro_training_convergence.png") - -# ── Plot 2: Out-of-sample rollout ──────────────────────────────────────────── - -plt2 = plot(; xlabel="Iteration", ylabel="Rollout Cost (no deficit)", - title="Out-of-Sample Rollout", legend=:topright) -for m in methods_wanted - haskey(runs, m) || continue - vals = get_history(runs[m], "metrics/rollout_objective_no_deficit") - isempty(vals) && continue - eval_every = try - PC.pyconvert(Int, get(runs[m].config, "eval_every", 25)) - catch - 25 - end - iters = eval_every .* (1:length(vals)) - plot!(plt2, iters, vals; label=method_labels[m], color=method_colors[m], - marker=:circle, markersize=3) -end -savefig(plt2, joinpath(DOCS_ASSETS, "hydro_cost_comparison.png")) -println("Saved hydro_cost_comparison.png") - -# ── Plot 3: Target violation share ─────────────────────────────────────────── - -plt3 = plot(; xlabel="Iteration", ylabel="Violation Share", - title="Target Violation Share", legend=:topright, ylims=(0, 0.3)) -for m in methods_wanted - haskey(runs, m) || continue - vals = get_history(runs[m], "metrics/rollout_target_violation_share") - isempty(vals) && continue - eval_every = try - PC.pyconvert(Int, get(runs[m].config, "eval_every", 25)) - catch - 25 - end - iters = eval_every .* (1:length(vals)) - plot!(plt3, iters, vals; label=method_labels[m], color=method_colors[m], - marker=:circle, markersize=3) -end -savefig(plt3, joinpath(DOCS_ASSETS, "hydro_violation_share.png")) -println("Saved hydro_violation_share.png") - -# ── Summary table ──────────────────────────────────────────────────────────── - -println("\n" * "="^80) -println("Summary Table") -println("="^80) -println(rpad("Method", 30), rpad("Last Loss", 15), rpad("Rollout", 15), - rpad("Violation", 12), rpad("Steps", 8)) -println("-"^80) -for m in methods_wanted - haskey(runs, m) || continue - r = runs[m] - summ = r.summary - loss = try round(PC.pyconvert(Float64, get(summ, "metrics/loss", nothing)); digits=0) catch; nothing end - rollout = try round(PC.pyconvert(Float64, get(summ, "metrics/rollout_objective_no_deficit", nothing)); digits=0) catch; nothing end - violation = try round(PC.pyconvert(Float64, get(summ, "metrics/rollout_target_violation_share", nothing)); digits=4) catch; nothing end - steps = try PC.pyconvert(Int, get(summ, "_step", nothing)) catch; nothing end - println(rpad(method_labels[m], 30), - rpad(isnothing(loss) ? "-" : string(loss), 15), - rpad(isnothing(rollout) ? "-" : string(rollout), 15), - rpad(isnothing(violation) ? "-" : string(violation), 12), - rpad(isnothing(steps) ? "-" : string(steps), 8)) -end -println("="^80) diff --git a/examples/HydroPowerModels/eval_jump_de.jl b/examples/HydroPowerModels/eval_jump_de.jl deleted file mode 100644 index 1a72412..0000000 --- a/examples/HydroPowerModels/eval_jump_de.jl +++ /dev/null @@ -1,143 +0,0 @@ -# eval_jump_de.jl -# -# Run the Bolivia hydro deterministic equivalent (ACPPowerModel, JuMP+Ipopt) -# with a constant policy (targets = TARGET_FRAC × max_volume) and a seeded -# inflow scenario. Saves the reference results to: -# • ./bolivia/jump_de_reference.jld2 (local copy) -# • EXA_CASE_DIR/jump_de_reference.jld2 (ExaGPU repo, for eval_exa_de.jl) -# -# Usage (SLURM job, must have DecisionRules.jl packages loaded): -# julia --project=../.. eval_jump_de.jl - -using DecisionRules -using Random -using JLD2 -using JuMP -using CUDA: CUDA -using MadNLP -using MadNLPGPU -using KernelAbstractions -using CUDSS_jll - -const SCRIPT_DIR = dirname(@__FILE__) -const CASE_DIR = joinpath(SCRIPT_DIR, "bolivia") -const EXA_CASE_DIR = "/storage/home/hcoda1/9/arosemberg3/scratch/DecisionRulesExaGPU.jl/examples/HydroPowerModels/bolivia" - -include(joinpath(SCRIPT_DIR, "load_hydropowermodels.jl")) - -const NUM_STAGES = 96 -const SEED = 42 -const FORMULATION = "ACPPowerModel" -const TARGET_FRAC = 0.6 # constant target = TARGET_FRAC × max_volume - -# ── Build subproblems ────────────────────────────────────────────────────────── - -@info "Building HydroPowerModels ($FORMULATION, T=$NUM_STAGES)..." - -sub, state_in, state_out, uncert, initial_state, max_volume = build_hydropowermodels( - CASE_DIR, FORMULATION * ".mof.json"; num_stages=NUM_STAGES, penalty_l2=:auto -) -nHyd = length(initial_state) -@info " nHyd=$nHyd num_stages=$NUM_STAGES" - -# ── Build deterministic equivalent ──────────────────────────────────────────── - -@info "Building deterministic equivalent..." -Det_model = Model(MadNLP.Optimizer) # for sparse problems - -set_optimizer_attribute(Det_model, "array_type", CUDA.CuArray) -set_optimizer_attribute(Det_model, "linear_solver", MadNLPGPU.CUDSSSolver) -set_optimizer_attribute(Det_model, "print_level", MadNLP.ERROR) -set_optimizer_attribute(Det_model, "barrier", MadNLP.LOQOUpdate()) - -Det_model, uncert_de = DecisionRules.deterministic_equivalent!( - Det_model, sub, state_in, state_out, Float64.(initial_state), uncert -) - -# ── Fix scenario (seeded random) ────────────────────────────────────────────── - -Random.seed!(SEED) -base_sample = DecisionRules.sample(uncert_de) -base_values = [[u[2] for u in stage_u] for stage_u in base_sample] - -# Vector{Vector{Tuple{VariableRef, Float64}}} — one scalar per uncertainty/stage -uncertainties_de = [ - [(stage_u[i][1], base_values[t][i]) for i in eachindex(stage_u)] for - (t, stage_u) in enumerate(uncert_de) -] - -# Flat inflow vector (stage-major): index (t-1)*nHyd + i → inflow[t, hydro i] -inflows_flat = Float64.([base_values[t][i] for t in 1:NUM_STAGES for i in 1:nHyd]) - -# ── Constant policy ──────────────────────────────────────────────────────────── -# simulate_states expects decision_rule(vcat(uncertainty_t, prev_state)) → next_state -# Our constant policy ignores both and always returns TARGET_FRAC × max_volume. - -const_target = Float64.(max_volume) .* TARGET_FRAC -const_policy = _ -> const_target - -# Returns T+1 elements: [initial_state, const_target, ..., const_target] -# simulate_multistage uses states[1] for x0 and states[t+1] as target for stage t. -states_policy = DecisionRules.simulate_states( - Float64.(initial_state), uncertainties_de, const_policy -) - -# Flat target vector (stage-major, same layout as inflows_flat) -targets_flat = Float64.([const_target[i] for t in 1:NUM_STAGES for i in 1:nHyd]) - -# ── Solve ────────────────────────────────────────────────────────────────────── - -@info "Solving DE (seed=$SEED, constant targets @ $(Int(TARGET_FRAC*100))% max_vol)..." -obj = DecisionRules.simulate_multistage( - Det_model, state_in, state_out, uncertainties_de, states_policy -) -@info " Objective: $(round(obj; digits=4))" - -# ── Extract reservoir trajectory (nHyd × (T+1)) ─────────────────────────────── -# After deterministic_equivalent!, state_out[t][i][2] is the DE VariableRef for -# the reservoir level at stage t, hydro i. value() returns the solved optimum. - -reservoir = zeros(Float64, nHyd, NUM_STAGES + 1) -reservoir[:, 1] = Float64.(initial_state) -for t in 1:NUM_STAGES - reservoir[:, t + 1] = Float64.([value(pair[2]) for pair in state_out[t]]) -end - -# ── Save ─────────────────────────────────────────────────────────────────────── - -function save_reference(outfile) - jldsave( - outfile; - objective=obj, - reservoir=reservoir, # nHyd × (T+1), rows=units, cols=stages 0..T - initial_state=Float64.(initial_state), - inflows_flat=inflows_flat, # length T*nHyd, stage-major - targets_flat=targets_flat, # length T*nHyd, stage-major - max_volume=Float64.(max_volume), - num_stages=NUM_STAGES, - nHyd=nHyd, - seed=SEED, - target_frac=TARGET_FRAC, - formulation=FORMULATION, - ) - @info "Saved reference to: $outfile" -end - -save_reference(joinpath(CASE_DIR, "jump_de_reference.jld2")) - -if isdir(EXA_CASE_DIR) - save_reference(joinpath(EXA_CASE_DIR, "jump_de_reference.jld2")) -else - @warn "ExaGPU bolivia dir not found: $EXA_CASE_DIR\n" * - "Copy bolivia/jump_de_reference.jld2 there manually before running eval_exa_de.jl" -end - -# ── Print summary ────────────────────────────────────────────────────────────── - -println("\nObjective: ", round(obj; digits=4)) -println("\nReservoir trajectory (end-of-stage, by hydro unit):") -println(" Stage: ", join(lpad.(0:NUM_STAGES, 9))) -for r in 1:nHyd - vals = round.(reservoir[r, :]; digits=1) - println(" Hydro $r: ", join(lpad.(vals, 9))) -end diff --git a/examples/HydroPowerModels/evaluate_hydro_policies.jl b/examples/HydroPowerModels/evaluate_hydro_policies.jl deleted file mode 100644 index cf59032..0000000 --- a/examples/HydroPowerModels/evaluate_hydro_policies.jl +++ /dev/null @@ -1,234 +0,0 @@ -# Evaluate pre-trained TS-DDR and TS-LDR policies on the Bolivia LTHD problem -# using stage-wise rollout under the ACP formulation with a fixed scenario set. -# -# This produces an apples-to-apples comparison across all methods using the -# same evaluation protocol: -# - stage-wise AC-OPF subproblems (Ipopt) -# - realized-state feedback (closed-loop / deployment semantics) -# - same seed and number of out-of-sample scenarios -# - operational cost excluding target-deficit penalty -# -# The script auto-discovers saved .jld2 checkpoints and reconstructs the -# correct policy architecture (LDR vs DDR) from the filename. -# Results are written to eval_costs.csv. -# -# Usage: -# julia --project=. evaluate_hydro_policies.jl [NUM_SIMULATIONS] -# -# Environment overrides: -# DR_EVAL_SIMULATIONS=100 number of out-of-sample scenarios -# DR_EVAL_SEED=1221 random seed for scenario generation - -using DecisionRules -using Statistics -using Random -using Flux -using Ipopt -using DiffOpt -using JLD2 -using JuMP -using CSV -using DataFrames - -const HYDRO_DIR = dirname(@__FILE__) -include(joinpath(HYDRO_DIR, "load_hydropowermodels.jl")) - -const CASE_NAME = "bolivia" -const FORMULATION = "ACPPowerModel" -const FORMULATION_FILE = FORMULATION * ".mof.json" -const NUM_STAGES = 96 -const NUM_SIMULATIONS = parse(Int, get(ENV, "DR_EVAL_SIMULATIONS", - length(ARGS) >= 1 ? ARGS[1] : "100")) -const SEED = parse(Int, get(ENV, "DR_EVAL_SEED", "1221")) - -const CASE_DIR = joinpath(HYDRO_DIR, CASE_NAME) -const OUT_DIR = joinpath(CASE_DIR, FORMULATION) -const MODEL_DIR = joinpath(OUT_DIR, "models") - -println("="^60) -println("Policy Evaluation (TS-DDR + TS-LDR)") -println("="^60) -println("Case: ", CASE_NAME) -println("Formulation: ", FORMULATION) -println("Stages: ", NUM_STAGES) -println("Simulations: ", NUM_SIMULATIONS) -println("Seed: ", SEED) -println("="^60) - -# ── Build stage-wise subproblems ───────────────────────────────────────────── - -diff_optimizer = () -> DiffOpt.diff_optimizer( - optimizer_with_attributes( - Ipopt.Optimizer, "print_level" => 0, "linear_solver" => "mumps", - ), -) - -subproblems, state_params_in, state_params_out, uncertainty_samples, initial_state, max_volume = - build_hydropowermodels( - CASE_DIR, FORMULATION_FILE; - num_stages=NUM_STAGES, - optimizer=diff_optimizer, - penalty_l1=:auto, penalty_l2=:auto, - ) - -num_hydro = length(initial_state) -num_uncertainties = length(uncertainty_samples[1][1]) -num_inputs = DecisionRules.policy_input_dim(num_uncertainties, num_hydro) - -# ── Generate fixed scenario set ────────────────────────────────────────────── - -Random.seed!(SEED) -eval_scenarios = [DecisionRules.sample(uncertainty_samples) for _ in 1:NUM_SIMULATIONS] - -# ── Discover saved models ──────────────────────────────────────────────────── -# -# Model files encode the training method and policy type in their filename: -# *-deteq-* → DDR trained with deterministic equivalent -# *-subproblems-* → DDR trained with stage-wise decomposition -# *-shooting-* → DDR trained with multiple shooting -# *-ldr-* → LDR (linear decision rule) -# -# DDR models use state_conditioned_policy (LSTM [128,128], sigmoid). -# LDR models use dense_multilayer_nn (identity activation, [64,64]). -# The most recent file (by lexicographic sort on timestamps) is selected -# for each method. - -struct PolicySpec - label::String - model_file::String - is_ldr::Bool -end - -function _method_variant(base) - method = if contains(base, "ldr") - "ldr" - elseif contains(base, "shooting") - "shooting" - elseif contains(base, "subproblems") - "subproblems" - elseif contains(base, "deteq") - "deteq" - else - return nothing - end - clip_tag = contains(base, "clip") ? "-clip" : "" - sched_tag = contains(base, "anneal") ? "-anneal" : - contains(base, "const") ? "-const" : "" - return method * clip_tag * sched_tag -end - -function _variant_label(variant) - labels = Dict( - "subproblems-anneal" => "Subproblems (anneal)", - "subproblems-clip-anneal" => "Subproblems (clip, anneal)", - "subproblems-const" => "Subproblems (const)", - "subproblems-clip-const" => "Subproblems (clip, const)", - "subproblems" => "Subproblems", - "shooting-anneal" => "Shooting w=12 (anneal)", - "shooting-clip-anneal" => "Shooting w=12 (clip, anneal)", - "shooting" => "Shooting w=12", - "deteq-anneal" => "DE (anneal)", - "deteq-clip-anneal" => "DE (clip, anneal)", - "deteq" => "DE", - "ldr" => "TS-LDR", - ) - return get(labels, variant, variant) -end - -function discover_policies(model_dir) - files = sort(filter(f -> endswith(f, ".jld2"), readdir(model_dir; join=true))) - best = Dict{String,Tuple{String,Bool}}() - for f in files - base = basename(f) - variant = _method_variant(base) - isnothing(variant) && continue - is_ldr = contains(base, "ldr") - best[variant] = (f, is_ldr) - end - specs = PolicySpec[] - for (variant, (path, is_ldr)) in sort(collect(best); by=first) - push!(specs, PolicySpec(_variant_label(variant), path, is_ldr)) - end - return specs -end - -function build_policy(spec::PolicySpec, num_inputs, num_hydro, num_uncertainties) - if spec.is_ldr - return dense_multilayer_nn(num_inputs, num_hydro, Int64[64, 64]; activation=identity) - else - return state_conditioned_policy( - num_uncertainties, num_hydro, num_hydro, Int64[128, 128]; - activation=sigmoid, encoder_type=Flux.LSTM, - ) - end -end - -policies = discover_policies(MODEL_DIR) -println("\nDiscovered policies:") -for p in policies - tag = p.is_ldr ? " (LDR)" : " (DDR)" - println(" ", p.label, tag, " → ", basename(p.model_file)) -end - -# ── Evaluate each policy ───────────────────────────────────────────────────── - -results = DataFrame() - -for spec in policies - println("\nEvaluating: ", spec.label) - - models = build_policy(spec, num_inputs, num_hydro, num_uncertainties) - model_state = JLD2.load(spec.model_file, "model_state") - Flux.loadmodel!(models, model_state) - - objectives_no_deficit = Vector{Float64}(undef, NUM_SIMULATIONS) - objectives_total = Vector{Float64}(undef, NUM_SIMULATIONS) - - for i in 1:NUM_SIMULATIONS - Flux.reset!(models) - - objectives_total[i] = simulate_multistage( - subproblems, - state_params_in, - state_params_out, - initial_state, - eval_scenarios[i], - models; - ) - - objectives_no_deficit[i] = DecisionRules.get_objective_no_target_deficit(subproblems) - end - - violation_share = 1.0 - mean(objectives_no_deficit) / mean(objectives_total) - - println(" Mean cost (no deficit): ", round(mean(objectives_no_deficit); digits=1)) - println(" Std: ", round(std(objectives_no_deficit); digits=1)) - println(" Violation share: ", round(violation_share * 100; digits=2), "%") - - results[!, spec.label] = objectives_no_deficit -end - -# ── Write results ──────────────────────────────────────────────────────────── - -costs_file = joinpath(OUT_DIR, "eval_costs.csv") -CSV.write(costs_file, results) -println("\nSaved: ", costs_file) - -# ── Summary table ──────────────────────────────────────────────────────────── - -println("\n", "="^70) -println(rpad("Method", 35), rpad("Mean", 12), rpad("Std", 12), "N") -println("-"^70) -for col in names(results) - vals = results[!, col] - println( - rpad(col, 35), - rpad(string(round(mean(vals); digits=1)), 12), - rpad(string(round(std(vals); digits=1)), 12), - length(vals), - ) -end -println("="^70) -println("\nNote: SDDP results are from sddp/simulate_sddp_policy.jl") -println("SDDP uses 126 stages (96 + 30 margin) to avoid end-of-horizon effects,") -println("while TS-DDR/TS-LDR use 96 stages. This gives SDDP a structural advantage.") diff --git a/examples/HydroPowerModels/export_subproblem_mof.jl b/examples/HydroPowerModels/export_subproblem_mof.jl deleted file mode 100644 index a4df07f..0000000 --- a/examples/HydroPowerModels/export_subproblem_mof.jl +++ /dev/null @@ -1,94 +0,0 @@ -# Export a single-stage OPF subproblem from HydroPowerModels as a .mof.json file. -# -# The DecisionRules training pipeline (load_hydropowermodels.jl) reads pre-exported -# .mof.json files rather than depending on HydroPowerModels.jl at training time. -# This script builds the SDDP model, extracts one subproblem from the policy graph, -# removes the unnamed slack variable that HydroPowerModels adds, and writes the -# clean JuMP model to disk. -# -# The exported files already ship with this repository under: -# bolivia/ACPPowerModel.mof.json -# bolivia/SOCWRConicPowerModel.mof.json -# bolivia/DCPPowerModel.mof.json -# case3/ACPPowerModel.mof.json -# -# Re-run this script only if: -# - The HydroPowerModels data (hydro.json, inflows.csv) has changed -# - A new power-flow formulation is needed -# - The HydroPowerModels.jl version changes the subproblem structure -# -# Requires: HydroPowerModels.jl, a compatible solver (Mosek, Gurobi, or MadNLP) -# -# Usage: -# julia export_subproblem_mof.jl [case] [formulation] -# julia export_subproblem_mof.jl bolivia ACPPowerModel -# julia export_subproblem_mof.jl bolivia SOCWRConicPowerModel - -using HydroPowerModels -using JuMP -using MosekTools - -# ── Configuration ───────────────────────────────────────────────────────────── - -case = length(ARGS) >= 1 ? ARGS[1] : "bolivia" -formulation_name = length(ARGS) >= 2 ? ARGS[2] : "ACPPowerModel" - -# Map string names to PowerModels types -FORMULATIONS = Dict( - "ACPPowerModel" => ACPPowerModel, - "SOCWRConicPowerModel" => SOCWRConicPowerModel, - "DCPPowerModel" => DCPPowerModel, -) - -formulation = FORMULATIONS[formulation_name] - -case_dir = joinpath(dirname(@__FILE__), case) -num_stages = 96 - -@info "Exporting subproblem" case formulation num_stages - -# ── Build the SDDP model ───────────────────────────────────────────────────── - -alldata = HydroPowerModels.parse_folder(case_dir) - -# Scale loads to match the training setup (Bolivia uses 60% load scaling) -for load in values(alldata[1]["powersystem"]["load"]) - load["qd"] = load["qd"] * 0.6 - load["pd"] = load["pd"] * 0.6 -end - -params = create_param(; - stages=num_stages, - model_constructor_grid=formulation, - post_method=PowerModels.build_opf, - optimizer=Mosek.Optimizer, -) - -m = hydro_thermal_operation(alldata, params) - -# ── Extract and clean one subproblem ────────────────────────────────────────── - -# Run a minimal simulation to populate the subproblem models -results = HydroPowerModels.simulate(m, 2) - -# The first stage subproblem is representative of all stages (same structure, -# different RHS values for inflows which load_hydropowermodels.jl sets via parameters) -model = m.forward_graph[1].subproblem - -# HydroPowerModels adds an unnamed slack variable — remove it before export -unnamed_idx = findfirst(v -> name(v) == "", all_variables(model)) -if !isnothing(unnamed_idx) - delete(model, all_variables(model)[unnamed_idx]) -end - -# ── Write to disk ───────────────────────────────────────────────────────────── - -outfile = joinpath(case_dir, formulation_name * ".mof.json") -JuMP.write_to_file(model, outfile) -@info "Exported subproblem to: $outfile" - -# Verify the file is readable -test_model = JuMP.read_from_file(outfile; use_nlp_block=false) -nvars = length(all_variables(test_model)) -ncons = length(all_constraints(test_model; include_variable_in_set_constraints=false)) -@info "Verification" variables = nvars constraints = ncons diff --git a/examples/HydroPowerModels/gen_inputs_l2O_hydropowermodels.jl b/examples/HydroPowerModels/gen_inputs_l2O_hydropowermodels.jl deleted file mode 100644 index 775fffd..0000000 --- a/examples/HydroPowerModels/gen_inputs_l2O_hydropowermodels.jl +++ /dev/null @@ -1,98 +0,0 @@ -using DecisionRules -using L2O -using JuMP -using UUIDs - -# Parameters -case_name = "case3" # bolivia, case3 -formulation = "ACPPowerModel" # SOCWRConicPowerModel, DCPPowerModel, ACPPowerModel -num_stages = 48 # 96, 48 -save_file = "$(case_name)-$(formulation)-h$(num_stages)" -formulation_file = formulation * ".mof.json" -batch_id = uuid1() - -# Build MSP - -HydroPowerModels_dir = dirname(@__FILE__) -include(joinpath(HydroPowerModels_dir, "load_hydropowermodels.jl")) -case_dir = joinpath(HydroPowerModels_dir, case_name) - -subproblems, state_params_in, state_params_out, uncertainty_samples, initial_state, max_volume = build_hydropowermodels( - joinpath(HydroPowerModels_dir, case_name), formulation_file; num_stages=num_stages -) - -det_equivalent, uncertainty_samples = DecisionRules.deterministic_equivalent!( - JuMP.Model(), - subproblems, - state_params_in, - state_params_out, - initial_state, - uncertainty_samples, -) - -# Remove state imposing constraints - -all_vars = all_variables(det_equivalent) -_deficit = all_vars[findall(x -> occursin("_deficit", name(x)), all_vars)] -state_params = vcat([[param[1] for param in params] for params in state_params_out]...) -state_vars = vcat([[param[2] for param in params] for params in state_params_out]...) -cons = JuMP.all_constraints(det_equivalent; include_variable_in_set_constraints=false) -for con in cons - obj = JuMP.constraint_object(con) - func = obj.func - _vars = if !(func isa Array) - keys(func.terms) - else - func - end - - if all([ - (_var in state_params) || (_var in _deficit) || (_var in state_vars) for - _var in _vars - ]) - delete(det_equivalent, con) - end -end - -for _var in _deficit - delete(det_equivalent, _var) -end - -for _var in state_params - delete(det_equivalent, _var) -end - -# The problem iterator -num_samples = 10000 -pairs = Dict{VariableRef,Vector{Float64}}() - -# initial_state -for (i, hyd_in) in enumerate(state_params_in[1]) - pairs[hyd_in] = fill(initial_state[i], num_samples) -end - -JuMP.write_to_file( - det_equivalent, joinpath(case_dir, formulation) * "_det_equivalent.mof.json" -) - -# inflow -recursive_merge(x::AbstractDict...) = merge(recursive_merge, x...) -inflow = vcat([[_var for _var in keys(dict)] for dict in uncertainty_samples]...) -pairs = merge( - pairs, Dict(inflow .=> [Vector{Float64}(undef, num_samples) for _ in 1:length(inflow)]) -) -for s in 1:num_samples - uncertainty_sample = sample(uncertainty_samples) - uncertainty_sample = recursive_merge(uncertainty_sample...) - for _var in inflow - pairs[_var][s] = uncertainty_sample[_var] - end -end - -problem_iterator = ProblemIterator(pairs) - -save( - problem_iterator, - joinpath(case_dir, case_name * "_" * formulation * "_input_" * string(batch_id)), - ArrowFile, -) diff --git a/examples/HydroPowerModels/load_hydropowermodels.jl b/examples/HydroPowerModels/load_hydropowermodels.jl deleted file mode 100644 index 613b425..0000000 --- a/examples/HydroPowerModels/load_hydropowermodels.jl +++ /dev/null @@ -1,110 +0,0 @@ -using JuMP -using CSV -using Tables -using JSON - -function find_reservoirs_and_inflow(model::JuMP.Model) - reservoir_in = find_variables(model, ["reservoir", "_in"]) - reservoir_out = find_variables(model, ["reservoir", "_out"]) - inflow = find_variables(model, ["inflow"]) - return reservoir_in, reservoir_out, inflow -end - -function read_inflow(file::String, nHyd::Int; num_stages=nothing) - allinflows = CSV.read(file, Tables.matrix; header=false) - nlin, ncol = size(allinflows) - if isnothing(num_stages) - num_stages = nlin - elseif num_stages > nlin - number_of_cycles = div(num_stages, nlin) + 1 - allinflows = vcat([allinflows for _ in 1:number_of_cycles]...) - end - nCen = Int(floor(ncol / nHyd)) - vector_inflows = Array{Array{Float64,2}}(undef, nHyd) - for i in 1:nHyd - vector_inflows[i] = allinflows[1:num_stages, ((i - 1) * nCen + 1):(i * nCen)] - end - return vector_inflows, nCen, num_stages -end - -function build_hydropowermodels( - case_folder::AbstractString, - subproblem_file::AbstractString; - num_stages=nothing, - penalty=nothing, - penalty_l1=nothing, - penalty_l2=nothing, - optimizer=nothing, -) - hydro_file = JSON.parsefile(joinpath(case_folder, "hydro.json"))["Hydrogenerators"] - nHyd = length(hydro_file) - vector_inflows, nCen, num_stages = read_inflow( - joinpath(case_folder, "inflows.csv"), nHyd; num_stages=num_stages - ) - initial_state = [hydro["initial_volume"] for hydro in hydro_file] - max_volume = [hydro["max_volume"] for hydro in hydro_file] - - subproblems = Vector{JuMP.Model}(undef, num_stages) - state_params_in = Vector{Vector{Any}}(undef, num_stages) - state_params_out = Vector{Vector{Tuple{Any,VariableRef}}}(undef, num_stages) - uncertainty_samples = Vector{Vector{Vector{Tuple{VariableRef,Float64}}}}( - undef, num_stages - ) - - for t in 1:num_stages - subproblems[t] = JuMP.read_from_file( - joinpath(case_folder, subproblem_file); use_nlp_block=false - ) - # Set optimizer if provided (for DiffOpt support) - if !isnothing(optimizer) - set_optimizer(subproblems[t], optimizer) - end - norm_deficit, _deficit = create_deficit!( - subproblems[t], - nHyd; - penalty=penalty, - penalty_l1=penalty_l1, - penalty_l2=penalty_l2, - ) - # delete fix constraints - for con in JuMP.all_constraints(subproblems[t], VariableRef, MOI.EqualTo{Float64}) - delete(subproblems[t], con) - end - state_params_in[t], state_param_out, inflow = find_reservoirs_and_inflow( - subproblems[t] - ) - state_params_in[t] = variable_to_parameter.(subproblems[t], state_params_in[t]) - state_params_out[t] = [ - variable_to_parameter(subproblems[t], state_param_out[i]; deficit=_deficit[i]) - for i in 1:nHyd - ] - # Joint scenarios: all hydro units share the same scenario index ω, - # preserving the spatial correlation in the historical inflow data. - inflow_params = [variable_to_parameter(subproblems[t], inflow[i]) for i in 1:nHyd] - joint_scenarios = [ - [(inflow_params[i], vector_inflows[i][t, ω] + 0.0) for i in 1:nHyd] - for ω in 1:nCen - ] - uncertainty_samples[t] = joint_scenarios - end - - return subproblems, - state_params_in, state_params_out, uncertainty_samples, initial_state, - max_volume -end - -function ensure_feasibility_cap(state_out, state_in, uncertainty, max_volume) - state_out = max.(state_out, 0) - state_out = min.(state_out, state_in .+ uncertainty) - state_out = min.(state_out, max_volume) - return state_out -end - -function ensure_feasibility_double_softplus(state_out, state_in, uncertainty, max_volume) - actual_max = min.(max_volume, state_in .+ uncertainty) - return softplus.(state_out .- 0.0) - softplus.(state_out .- actual_max) -end - -function ensure_feasibility_sigmoid(state_out, state_in, uncertainty, max_volume) - return sigmoid.(state_out) .* min.(max_volume, state_in .+ uncertainty) -end diff --git a/examples/HydroPowerModels/sddp/Project.toml b/examples/HydroPowerModels/sddp/Project.toml deleted file mode 100644 index 2aac97c..0000000 --- a/examples/HydroPowerModels/sddp/Project.toml +++ /dev/null @@ -1,11 +0,0 @@ -[deps] -CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" -Clarabel = "61c947e1-3e6d-4ee4-985a-eec8c727bd6e" -DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" -HydroPowerModels = "1bf2e10f-7293-4f36-bafb-f7584ca75eae" -JuMP = "4076af6c-e467-56ae-b986-b466b2749572" -MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6" -Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -PowerModels = "c36e90e8-916a-50a6-bd94-075b64ef4655" -SDDP = "f4570300-c277-11e8-125c-4912f86ce65d" -Wandb = "ad70616a-06c9-5745-b1f1-6a5f42545108" diff --git a/examples/HydroPowerModels/sddp/run_sddp.jl b/examples/HydroPowerModels/sddp/run_sddp.jl deleted file mode 100644 index 6643640..0000000 --- a/examples/HydroPowerModels/sddp/run_sddp.jl +++ /dev/null @@ -1,168 +0,0 @@ -# SDDP baseline: train and simulate SDDP policy on the Bolivia LTHD problem -# using a consistent convex SOCWRConic formulation. - -using Clarabel -using HydroPowerModels -using JuMP -using Logging -using PowerModels -using Random -using SDDP -using Statistics -using Wandb, Dates - -const SEED = parse(Int, get(ENV, "DR_SDDP_SEED", "1221")) -const CASE = get(ENV, "DR_SDDP_CASE", "bolivia") -const HYDRO_DIR = dirname(@__DIR__) -const CASE_DIR = joinpath(HYDRO_DIR, CASE) -const RM_STAGES = parse(Int, get(ENV, "DR_SDDP_RM_STAGES", "30")) -const NUM_STAGES = parse(Int, get(ENV, "DR_SDDP_NUM_STAGES", string(96 + RM_STAGES))) -const ITERATION_LIMIT = parse(Int, get(ENV, "DR_SDDP_ITERATION_LIMIT", "200")) -const NUM_SIMULATIONS = parse(Int, get(ENV, "DR_SDDP_SIMULATIONS", "300")) -const STAT_REPLICATIONS = parse(Int, get(ENV, "DR_SDDP_STAT_REPLICATIONS", "300")) -const STAT_PERIOD = parse(Int, get(ENV, "DR_SDDP_STAT_PERIOD", "50")) -const FORMULATION = SOCWRConicPowerModel -const save_file = "SDDP-$(CASE)-$(FORMULATION)-$(FORMULATION)-h$(NUM_STAGES)-$(Dates.now())" -const CUTS_FILE = joinpath( - CASE_DIR, - string(FORMULATION), - string(FORMULATION) * "-" * string(FORMULATION) * ".cuts.json", -) - -function clarabel_optimizer() - return Clarabel.Optimizer(; - verbose=false, - max_iter=parse(Int, get(ENV, "DR_SDDP_CLARABEL_MAX_ITER", "1000")), - tol_gap_abs=parse(Float64, get(ENV, "DR_SDDP_CLARABEL_TOL", "1e-7")), - tol_gap_rel=parse(Float64, get(ENV, "DR_SDDP_CLARABEL_TOL", "1e-7")), - tol_feas=parse(Float64, get(ENV, "DR_SDDP_CLARABEL_TOL", "1e-7")), - ) -end - -mutable struct WandBLog <: SDDP.AbstractStoppingRule - cuts_file::String - lg -end - -SDDP.stopping_rule_status(::WandBLog) = :not_solved - -function SDDP.convergence_test( - policy::SDDP.PolicyGraph, - log::Vector{SDDP.Log}, - rule::WandBLog, -) - mkpath(dirname(rule.cuts_file)) - SDDP.write_cuts_to_file(policy, rule.cuts_file) - latest = log[end] - Wandb.log( - rule.lg, - Dict( - "batch" => length(log), - "metrics/loss" => latest.bound, - "metrics/rollout_realized_objective_no_deficit" => latest.simulation_value, - ), - ) - println( - "iteration=$(length(log)) bound=$(latest.bound) simulation_value=$(latest.simulation_value)", - ) - flush(stdout) - return false -end - -function load_case_data() - alldata = HydroPowerModels.parse_folder(CASE_DIR) - for load in values(alldata[1]["powersystem"]["load"]) - load["qd"] *= 0.6 - load["pd"] *= 0.6 - end - return alldata -end - -function main() - println("Run: ", save_file) - println("Case directory: ", CASE_DIR) - println("Formulation: ", FORMULATION, " with Clarabel") - - Random.seed!(SEED) - mkpath(dirname(CUTS_FILE)) - alldata = load_case_data() - lg = WandbLogger(; - project="RL", - name=save_file, - save_code=false, - config=Dict( - "case_name" => CASE, - "training_method" => "sddp_consistent", - "formulation" => string(FORMULATION), - "solver" => "Clarabel", - "num_stages" => NUM_STAGES, - "rm_stages" => RM_STAGES, - "iteration_limit" => ITERATION_LIMIT, - "num_simulations" => NUM_SIMULATIONS, - "stat_replications" => STAT_REPLICATIONS, - "stat_period" => STAT_PERIOD, - "seed" => SEED, - ), - ) - params = create_param(; - stages=NUM_STAGES, - model_constructor_grid=FORMULATION, - post_method=PowerModels.build_opf, - optimizer=clarabel_optimizer, - ) - model = hydro_thermal_operation(alldata, params) - - if isfile(CUTS_FILE) - println("Loading existing cuts: ", CUTS_FILE) - SDDP.read_cuts_from_file(model.forward_graph, CUTS_FILE) - end - - stopping_rules = SDDP.AbstractStoppingRule[WandBLog(CUTS_FILE, lg)] - if STAT_REPLICATIONS > 0 - push!( - stopping_rules, - SDDP.Statistical(; - num_replications=STAT_REPLICATIONS, - iteration_period=STAT_PERIOD, - ), - ) - end - - start_time = time() - HydroPowerModels.train( - model; - iteration_limit=ITERATION_LIMIT, - stopping_rules=stopping_rules, - ) - elapsed = time() - start_time - bound = SDDP.calculate_bound(model.forward_graph) - println("Termination status: ", SDDP.termination_status(model.forward_graph)) - println("Elapsed seconds: ", elapsed) - println("Bound: ", bound) - - SDDP.write_cuts_to_file(model.forward_graph, CUTS_FILE) - println("Saved cuts: ", CUTS_FILE) - - Random.seed!(SEED) - results = HydroPowerModels.simulate(model, NUM_SIMULATIONS) - objective_values = [ - sum(results[:simulations][i][t][:stage_objective] for t in 1:(NUM_STAGES - RM_STAGES)) - for i in 1:length(results[:simulations]) - ] - final_loss = mean(objective_values) - println("Mean Sim: ", final_loss) - Wandb.log( - lg, - Dict( - "batch" => ITERATION_LIMIT, - "metrics/loss" => bound, - "metrics/final_loss" => final_loss, - "metrics/rollout_realized_objective_no_deficit" => final_loss, - "metrics/final_rollout_realized_objective_no_deficit" => final_loss, - "metrics/elapsed_seconds" => elapsed, - ), - ) - close(lg) -end - -main() diff --git a/examples/HydroPowerModels/sddp/run_sddp_inconsistent.jl b/examples/HydroPowerModels/sddp/run_sddp_inconsistent.jl deleted file mode 100644 index 0035912..0000000 --- a/examples/HydroPowerModels/sddp/run_sddp_inconsistent.jl +++ /dev/null @@ -1,196 +0,0 @@ -# SDDP baseline with inconsistent formulations: train SDDP with a convex -# SOCWRConic backward-pass formulation and an AC forward-pass formulation, -# then simulate the resulting policy under the AC model. -# -# Environment overrides for local smoke tests: -# DR_SDDP_ITERATION_LIMIT=2 -# DR_SDDP_SIMULATIONS=2 -# DR_SDDP_STAT_REPLICATIONS=2 -# DR_SDDP_STAT_PERIOD=1 - -using Clarabel -using HydroPowerModels -using JuMP -using Logging -using MadNLP -using PowerModels -using Random -using SDDP -using Statistics -using Wandb, Dates - -const SEED = parse(Int, get(ENV, "DR_SDDP_SEED", "1221")) -const CASE = get(ENV, "DR_SDDP_CASE", "bolivia") -const HYDRO_DIR = dirname(@__DIR__) -const CASE_DIR = joinpath(HYDRO_DIR, CASE) -const RM_STAGES = parse(Int, get(ENV, "DR_SDDP_RM_STAGES", "30")) -const NUM_STAGES = parse(Int, get(ENV, "DR_SDDP_NUM_STAGES", string(96 + RM_STAGES))) -const ITERATION_LIMIT = parse(Int, get(ENV, "DR_SDDP_ITERATION_LIMIT", "2000")) -const NUM_SIMULATIONS = parse(Int, get(ENV, "DR_SDDP_SIMULATIONS", "300")) -const STAT_REPLICATIONS = parse(Int, get(ENV, "DR_SDDP_STAT_REPLICATIONS", "300")) -const STAT_PERIOD = parse(Int, get(ENV, "DR_SDDP_STAT_PERIOD", "200")) - -const FORMULATION_BACKWARD = SOCWRConicPowerModel -const FORMULATION_FORWARD = ACPPowerModel -const save_file = "SDDP-$(CASE)-$(FORMULATION_FORWARD)-$(FORMULATION_BACKWARD)-h$(NUM_STAGES)-$(Dates.now())" -const CUTS_DIR = joinpath(CASE_DIR, string(FORMULATION_FORWARD)) -const CUTS_FILE = joinpath( - CUTS_DIR, - string(FORMULATION_BACKWARD) * "-" * string(FORMULATION_FORWARD) * ".cuts.json", -) - -function clarabel_optimizer() - return Clarabel.Optimizer(; - verbose=false, - max_iter=parse(Int, get(ENV, "DR_SDDP_CLARABEL_MAX_ITER", "1000")), - tol_gap_abs=parse(Float64, get(ENV, "DR_SDDP_CLARABEL_TOL", "1e-7")), - tol_gap_rel=parse(Float64, get(ENV, "DR_SDDP_CLARABEL_TOL", "1e-7")), - tol_feas=parse(Float64, get(ENV, "DR_SDDP_CLARABEL_TOL", "1e-7")), - ) -end - -function madnlp_optimizer() - return MadNLP.Optimizer(; - print_level=parse(Int, get(ENV, "DR_SDDP_MADNLP_PRINT_LEVEL", "0")), - ) -end - -mutable struct WandBLog <: SDDP.AbstractStoppingRule - cuts_file::String - lg -end - -SDDP.stopping_rule_status(::WandBLog) = :not_solved - -function SDDP.convergence_test( - policy::SDDP.PolicyGraph, - log::Vector{SDDP.Log}, - rule::WandBLog, -) - mkpath(dirname(rule.cuts_file)) - SDDP.write_cuts_to_file(policy, rule.cuts_file) - latest = log[end] - Wandb.log( - rule.lg, - Dict( - "batch" => length(log), - "metrics/loss" => latest.bound, - "metrics/rollout_realized_objective_no_deficit" => latest.simulation_value, - ), - ) - println( - "iteration=$(length(log)) bound=$(latest.bound) simulation_value=$(latest.simulation_value)", - ) - flush(stdout) - return false -end - -function load_case_data() - alldata = HydroPowerModels.parse_folder(CASE_DIR) - for load in values(alldata[1]["powersystem"]["load"]) - load["qd"] *= 0.6 - load["pd"] *= 0.6 - end - return alldata -end - -function main() - println("Run: ", save_file) - println("Case directory: ", CASE_DIR) - println("Stages: ", NUM_STAGES, " (reporting first ", NUM_STAGES - RM_STAGES, ")") - println("Backward formulation: ", FORMULATION_BACKWARD, " with Clarabel") - println("Forward formulation: ", FORMULATION_FORWARD, " with MadNLP") - println("Iteration limit: ", ITERATION_LIMIT) - println("Simulations: ", NUM_SIMULATIONS) - - Random.seed!(SEED) - mkpath(CUTS_DIR) - alldata = load_case_data() - lg = WandbLogger(; - project="RL", - name=save_file, - save_code=false, - config=Dict( - "case_name" => CASE, - "training_method" => "sddp_inconsistent", - "backward_formulation" => string(FORMULATION_BACKWARD), - "forward_formulation" => string(FORMULATION_FORWARD), - "backward_solver" => "Clarabel", - "forward_solver" => "MadNLP", - "num_stages" => NUM_STAGES, - "rm_stages" => RM_STAGES, - "iteration_limit" => ITERATION_LIMIT, - "num_simulations" => NUM_SIMULATIONS, - "stat_replications" => STAT_REPLICATIONS, - "stat_period" => STAT_PERIOD, - "seed" => SEED, - ), - ) - - params = create_param(; - stages=NUM_STAGES, - model_constructor_grid=FORMULATION_BACKWARD, - model_constructor_grid_forward=FORMULATION_FORWARD, - post_method=PowerModels.build_opf, - optimizer=clarabel_optimizer, - optimizer_forward=madnlp_optimizer, - ) - - model = hydro_thermal_operation(alldata, params) - - if isfile(CUTS_FILE) - println("Loading existing cuts: ", CUTS_FILE) - SDDP.read_cuts_from_file(model.forward_graph, CUTS_FILE) - end - - stopping_rules = SDDP.AbstractStoppingRule[WandBLog(CUTS_FILE, lg)] - if STAT_REPLICATIONS > 0 - push!( - stopping_rules, - SDDP.Statistical(; - num_replications=STAT_REPLICATIONS, - iteration_period=STAT_PERIOD, - ), - ) - end - - start_time = time() - HydroPowerModels.train( - model; - iteration_limit=ITERATION_LIMIT, - stopping_rules=stopping_rules, - ) - elapsed = time() - start_time - - status = SDDP.termination_status(model.forward_graph) - bound = SDDP.calculate_bound(model.forward_graph) - println("Termination status: ", status) - println("Elapsed seconds: ", elapsed) - println("Bound: ", bound) - - SDDP.write_cuts_to_file(model.forward_graph, CUTS_FILE) - println("Saved cuts: ", CUTS_FILE) - - Random.seed!(SEED) - results = HydroPowerModels.simulate(model, NUM_SIMULATIONS) - objective_values = [ - sum(results[:simulations][i][t][:stage_objective] for t in 1:(NUM_STAGES - RM_STAGES)) - for i in 1:length(results[:simulations]) - ] - final_loss = mean(objective_values) - println("Mean Sim: ", final_loss) - Wandb.log( - lg, - Dict( - "batch" => ITERATION_LIMIT, - "metrics/loss" => bound, - "metrics/final_loss" => final_loss, - "metrics/rollout_realized_objective_no_deficit" => final_loss, - "metrics/final_rollout_realized_objective_no_deficit" => final_loss, - "metrics/elapsed_seconds" => elapsed, - ), - ) - close(lg) -end - -main() diff --git a/examples/HydroPowerModels/sddp/simulate_sddp_policy.jl b/examples/HydroPowerModels/sddp/simulate_sddp_policy.jl deleted file mode 100644 index a392de6..0000000 --- a/examples/HydroPowerModels/sddp/simulate_sddp_policy.jl +++ /dev/null @@ -1,161 +0,0 @@ -# Simulate a pre-trained SDDP policy (cuts from run_sddp_inconsistent.jl) under -# the ACP formulation and produce comparison plots/CSVs against TS-DDR baselines. -using MadNLP -using HydroPowerModels -using JuMP -using PowerModels -using Statistics -using SDDP: SDDP - -using Random -seed = 1221 - -# Load case -case = "bolivia" -case_dir = joinpath(dirname(@__DIR__), case) -alldata = HydroPowerModels.parse_folder(case_dir); -for load in values(alldata[1]["powersystem"]["load"]) - load["qd"] = load["qd"] * 0.6 - load["pd"] = load["pd"] * 0.6 -end -rm_stages = 30 -num_stages = 96 + rm_stages -formulation = ACPPowerModel -formulation_b = SOCWRConicPowerModel - -params = create_param(; - stages=num_stages, - model_constructor_grid=formulation, - post_method=PowerModels.build_opf, - optimizer=() -> MadNLP.Optimizer(; print_level=0), -); - -m = hydro_thermal_operation(alldata, params); - -# Load pre-trained cuts -SDDP.read_cuts_from_file( - m.forward_graph, - joinpath( - case_dir, - string(formulation), - string(formulation_b)*"-"*string(formulation)*".cuts.json", - ), -) - -# Simulate -Random.seed!(seed) -num_sim = 100 -results = HydroPowerModels.simulate(m, num_sim); - -# Plotting -nhyd = alldata[1]["hydro"]["nHyd"] -using Plots -using CSV -using DataFrames -volume_to_mw(volume, stage_hours; k=0.0036) = volume / (k * stage_hours) - -const SDDP_COL = "SDDP-SOC" -labels = ["TS-DDR"; "TS-LDR"; "SDDP-DCLL"; SDDP_COL] -colors = [:black :purple :red :orange] -markers = [:hline :+ :pixel :diamond] - -const DOCS_ASSETS = joinpath(dirname(@__DIR__), "..", "..", "docs", "src", "assets") -mkpath(DOCS_ASSETS) -out_dir = joinpath(case_dir, string(formulation)) - -# Volume trajectory -hydro_gen = [ - mean( - sum( - volume_to_mw(results[:simulations][i][t][:reservoirs][:reservoir][j].out, 1) for - j in 1:nhyd - ) for i in 1:num_sim - ) for t in 1:(num_stages - rm_stages) -] - -savefig( - plot( - hydro_gen; - legend=false, - xlabel="Stage", - ylabel="Volume (Hm3)", - title="$(case)-$(formulation_b)-$(formulation)", - ), - joinpath(out_dir, "SDDP-$(case)-$(formulation_b)-$(formulation)-Volume.png"), -) - -df = CSV.read(joinpath(out_dir, "MeanVolume.csv"), DataFrame; header=true) -df[!, SDDP_COL] = hydro_gen -CSV.write(joinpath(out_dir, "MeanVolume.csv"), df) - -savefig( - plot( - Matrix(df[!, labels]); - labels=permutedims(labels), - xlabel="Stage", - ylabel="Expected Volume (MWh)", - color=colors, - shape=markers, - title="Reservoir Volume Comparison", - ), - joinpath(DOCS_ASSETS, "hydro_volume_comparison.png"), -) - -# Thermal generation -num_gen = length(results[:simulations][1][1][:powersystem]["solution"]["gen"]) -hydro_idx = HydroPowerModels.idx_hydro(results[:data][1]) -thermal_gen = [ - mean( - sum( - results[:simulations][i][t][:powersystem]["solution"]["gen"]["$j"]["pg"] * - results[:data][1]["powersystem"]["baseMVA"] for - j in 1:num_gen if !(j in hydro_idx) - ) for i in 1:num_sim - ) for t in 1:(num_stages - rm_stages) -] - -savefig( - plot( - thermal_gen; - legend=false, - xlabel="Stage", - ylabel="Mwh", - title="Thermal-Generation $(case)-$(formulation_b)-$(formulation)", - ), - joinpath(out_dir, "SDDP-$(case)-$(formulation_b)-$(formulation)-thermal.png"), -) - -df = CSV.read(joinpath(out_dir, "MeanGeneration.csv"), DataFrame) -df[!, SDDP_COL] = thermal_gen -CSV.write(joinpath(out_dir, "MeanGeneration.csv"), df) - -savefig( - plot( - Matrix(df[!, labels]); - labels=permutedims(labels), - xlabel="Stage", - ylabel="Expected Thermal Generation (MWh)", - color=colors, - shape=markers, - title="Thermal Generation Comparison", - ), - joinpath(DOCS_ASSETS, "hydro_generation_comparison.png"), -) - -# Objective costs -objective_values = [ - sum(results[:simulations][i][t][:stage_objective] for t in 1:(num_stages - rm_stages)) - for i in 1:length(results[:simulations]) -] - -costs_file = joinpath(out_dir, "costs.csv") -if isfile(costs_file) - df = CSV.read(costs_file, DataFrame) - df[!, SDDP_COL] = objective_values -else - df = DataFrame(Symbol(SDDP_COL) => objective_values) -end -CSV.write(costs_file, df) - -println("Mean Sim: ", mean(objective_values)) -println("Std Sim: ", std(objective_values)) diff --git a/examples/HydroPowerModels/test_sampling_consistency.jl b/examples/HydroPowerModels/test_sampling_consistency.jl deleted file mode 100644 index 0bbe997..0000000 --- a/examples/HydroPowerModels/test_sampling_consistency.jl +++ /dev/null @@ -1,166 +0,0 @@ -# test_sampling_consistency.jl -# -# Verifies that DecisionRules.jl and the ExaModels companion package -# (DecisionRulesExa.jl) sample from the exact same inflow distribution -# as SDDP.jl for the Bolivia HydroPowerModels case. -# -# All three systems read the same inflows.csv and hydro.json files. -# The sampling contract is: -# -# At each stage t, draw one scenario index ω ∈ {1, …, nScenarios} -# uniformly at random. All hydro reservoirs receive the inflow from -# column ω of the historical data for their respective row t. -# Stages are sampled independently (no temporal correlation). -# -# This is SDDP.jl's `SDDP.parameterize` semantics: one ω per node, -# applied to all random variables in that node. -# -# What this script checks: -# 1. Both loaders parse inflows.csv into identical per-reservoir matrices. -# 2. Both samplers produce draws from the same support (only historically -# observed joint vectors, never cross-scenario combinations). -# 3. With the same RNG seed, both produce identical trajectories. -# -# Usage: -# julia --project=. test_sampling_consistency.jl (from this dir) -# julia --project=. test_sampling_consistency.jl /path/to/DecisionRulesExa.jl/examples/HydroPowerModels - -using Test -using Random -using CSV, Tables, JSON - -# ── Paths ──────────────────────────────────────────────────────────────────── - -const SCRIPT_DIR = dirname(@__FILE__) -const CASE_DIR = joinpath(SCRIPT_DIR, "bolivia") -const INFLOW_FILE = joinpath(CASE_DIR, "inflows.csv") -const HYDRO_FILE = joinpath(CASE_DIR, "hydro.json") - -const EXA_DIR = length(ARGS) >= 1 ? ARGS[1] : - joinpath(dirname(dirname(dirname(SCRIPT_DIR))), - "..", "DecisionRulesExa.jl", "examples", "HydroPowerModels") - -# ── 1. Parse inflows with both loaders ─────────────────────────────────────── - -# DecisionRules.jl loader (load_hydropowermodels.jl::read_inflow) -function dr_read_inflow(file, nHyd; num_stages=nothing) - allinflows = CSV.read(file, Tables.matrix; header=false) - nlin, ncol = size(allinflows) - if isnothing(num_stages) - num_stages = nlin - elseif num_stages > nlin - number_of_cycles = div(num_stages, nlin) + 1 - allinflows = vcat([allinflows for _ in 1:number_of_cycles]...) - end - nCen = Int(floor(ncol / nHyd)) - vector_inflows = [allinflows[1:num_stages, ((i-1)*nCen+1):(i*nCen)] for i in 1:nHyd] - return vector_inflows, nCen, num_stages -end - -# ExaModels loader (hydro_power_data.jl::load_hydro_data, inflow portion only) -function exa_read_inflow(file, nHyd; num_stages=nothing) - allinflows = CSV.read(file, Tables.matrix; header=false) - nrows, ncols = size(allinflows) - nScenarios = div(ncols, nHyd) - nStagesSample = isnothing(num_stages) ? nrows : num_stages - if !isnothing(num_stages) && num_stages > nrows - repeats = div(num_stages, nrows) + 1 - allinflows = vcat([allinflows for _ in 1:repeats]...) - end - allinflows = allinflows[1:nStagesSample, :] - scenario_inflows = [Float64.(allinflows[:, ((r-1)*nScenarios+1):(r*nScenarios)]) for r in 1:nHyd] - return scenario_inflows, nScenarios, nStagesSample -end - -# ── 2. Sampler implementations (extracted, no package dependencies) ────────── - -function dr_sample_joint(vector_inflows, nCen, T) - nHyd = length(vector_inflows) - trajectory = Vector{Vector{Float64}}(undef, T) - for t in 1:T - ω = rand(1:nCen) - trajectory[t] = [vector_inflows[r][t, ω] for r in 1:nHyd] - end - return trajectory -end - -function exa_sample_scenario(scenario_inflows, nScenarios, T) - nHyd = length(scenario_inflows) - nStagesSample = size(scenario_inflows[1], 1) - w = Vector{Float64}(undef, T * nHyd) - for t in 1:T - t_row = mod1(t, nStagesSample) - j = rand(1:nScenarios) - for r in 1:nHyd - w[(t-1)*nHyd + r] = scenario_inflows[r][t_row, j] - end - end - return w -end - -# ── Tests ──────────────────────────────────────────────────────────────────── - -hydro_json = JSON.parsefile(HYDRO_FILE)["Hydrogenerators"] -nHyd = length(hydro_json) -T = 96 - -@testset "Sampling consistency: DecisionRules vs Exa vs SDDP" begin - dr_inflows, dr_nCen, dr_T = dr_read_inflow(INFLOW_FILE, nHyd; num_stages=T) - exa_inflows, exa_nScen, exa_T = exa_read_inflow(INFLOW_FILE, nHyd; num_stages=T) - - @testset "identical inflow matrices" begin - @test dr_nCen == exa_nScen - @test dr_T == exa_T - for r in 1:nHyd - @test dr_inflows[r] == exa_inflows[r] - end - end - - @testset "same seed → identical trajectories" begin - for seed in [42, 123, 9999] - Random.seed!(seed) - dr_traj = dr_sample_joint(dr_inflows, dr_nCen, T) - - Random.seed!(seed) - exa_flat = exa_sample_scenario(exa_inflows, exa_nScen, T) - - for t in 1:T - for r in 1:nHyd - @test dr_traj[t][r] == exa_flat[(t-1)*nHyd + r] - end - end - end - end - - @testset "samples are always from historical scenarios (joint)" begin - valid_vectors = Set{Vector{Float64}}() - for t in 1:T, ω in 1:dr_nCen - push!(valid_vectors, [dr_inflows[r][t, ω] for r in 1:nHyd]) - end - - Random.seed!(42) - for _ in 1:500 - traj = dr_sample_joint(dr_inflows, dr_nCen, T) - for stage_vec in traj - @test stage_vec in valid_vectors - end - end - end - - @testset "uniform coverage of all scenarios" begin - Random.seed!(42) - N = 10_000 - counts = zeros(Int, dr_nCen) - for _ in 1:N - ω = rand(1:dr_nCen) - counts[ω] += 1 - end - for ω in 1:dr_nCen - freq = counts[ω] / N - expected = 1.0 / dr_nCen - @test abs(freq - expected) < 0.03 - end - end -end - -println("\nAll sampling consistency tests passed.") diff --git a/examples/HydroPowerModels/train_dr_hydropowermodels.jl b/examples/HydroPowerModels/train_dr_hydropowermodels.jl deleted file mode 100644 index ad6876c..0000000 --- a/examples/HydroPowerModels/train_dr_hydropowermodels.jl +++ /dev/null @@ -1,256 +0,0 @@ -# Train HydroPowerModels using Deterministic Equivalent formulation (GPU-enabled) -using DecisionRules -using Statistics -using Random -using Flux - -using Ipopt -using Wandb, Dates, Logging -using JLD2 -using DiffOpt -using JuMP -using MadNLP - -USE_GPU = try - using CUDA, CUDSS, MadNLPGPU - CUDA.functional() -catch - @warn "GPU packages not available — running on CPU" - false -end -@info "GPU status" USE_GPU - -HydroPowerModels_dir = dirname(@__FILE__) -include(joinpath(HydroPowerModels_dir, "load_hydropowermodels.jl")) - -# Functions - -function non_ensurance(x_out, x_in, uncertainty, max_volume) - return x_out -end - -# Parameters -case_name = "bolivia" # bolivia, case3 -formulation = "ACPPowerModel" # SOCWRConicPowerModel, DCPPowerModel, ACPPowerModel -num_stages = parse(Int, get(ENV, "DR_NUM_STAGES", "126")) -model_dir = joinpath(HydroPowerModels_dir, case_name, formulation, "models") -mkpath(model_dir) -solver_tag = USE_GPU ? "gpu" : "cpu" -formulation_file = formulation * ".mof.json" - -# Training parameters -num_epochs = parse(Int, get(ENV, "DR_NUM_EPOCHS", "80")) -num_batches = 100 -_num_train_per_batch = 1 -activation = sigmoid # tanh, identity, relu, sigmoid -layers = Int64[128, 128] -ensure_feasibility = non_ensurance -grad_clip = parse(Float32, get(ENV, "DR_GRAD_CLIP", "0")) -optimizers = if grad_clip > 0 - [Flux.Optimisers.OptimiserChain(Flux.Optimisers.ClipGrad(grad_clip), Flux.Adam())] -else - [Flux.Adam()] -end -pre_trained_model = nothing -penalty_l2 = :auto -penalty_l1 = :auto -penalty_schedule = if get(ENV, "DR_PENALTY_SCHEDULE", "annealed") == "annealed" - :default_annealed -else - nothing -end -clip_tag = grad_clip > 0 ? "-clip$(Int(grad_clip))" : "" -sched_tag = isnothing(penalty_schedule) ? "-const" : "-anneal" -save_file = "$(case_name)-$(formulation)-h$(num_stages)-deteq-$(solver_tag)$(clip_tag)$(sched_tag)-$(now())" -num_eval_scenarios = 4 -eval_every = 25 - -# Build MSP: subproblems for rollout evaluation (stage-wise, CPU, with DiffOpt) -diff_optimizer = - () -> DiffOpt.diff_optimizer( - optimizer_with_attributes( - Ipopt.Optimizer, - "print_level" => 0, - "linear_solver" => "mumps", - ), - ) -subproblems, state_params_in_sub, state_params_out_sub, uncertainty_samples_sub, initial_state, max_volume = build_hydropowermodels( - joinpath(HydroPowerModels_dir, case_name), - formulation_file; - num_stages=num_stages, - optimizer=diff_optimizer, - penalty_l1=penalty_l1, - penalty_l2=penalty_l2, -) - -# Build det-eq for training -subproblems_de, state_params_in, state_params_out, uncertainty_samples, _, _ = build_hydropowermodels( - joinpath(HydroPowerModels_dir, case_name), - formulation_file; - num_stages=num_stages, - penalty_l1=penalty_l1, - penalty_l2=penalty_l2, -) - -det_equivalent = Model(MadNLP.Optimizer) - -if USE_GPU - set_optimizer_attribute(det_equivalent, "array_type", CUDA.CuArray) - set_optimizer_attribute(det_equivalent, "linear_solver", MadNLPGPU.CUDSSSolver) - set_optimizer_attribute(det_equivalent, "print_level", MadNLP.ERROR) - set_optimizer_attribute(det_equivalent, "barrier", MadNLP.LOQOUpdate()) -else - set_optimizer_attribute(det_equivalent, "print_level", MadNLP.ERROR) - set_optimizer_attribute(det_equivalent, "barrier", MadNLP.LOQOUpdate()) - # set_optimizer_attribute(det_equivalent, "linear_solver", MadNLPGPU.LapackCPUSolver()) -end - -det_equivalent, uncertainty_samples = DecisionRules.deterministic_equivalent!( - det_equivalent, - subproblems_de, - state_params_in, - state_params_out, - initial_state, - uncertainty_samples, -) - -num_hydro = length(initial_state) - -# Logging -lg = WandbLogger(; - project="RL", - name=save_file, - save_code=false, - config=Dict( - "layers" => layers, - "activation" => string(activation), - "encoder_type" => "LSTM", - "ensure_feasibility" => string(ensure_feasibility), - "optimizer" => string(optimizers), - "grad_clip" => grad_clip, - "training_method" => "deterministic_equivalent", - "solver" => USE_GPU ? "MadNLP+CUDSS (GPU)" : "MadNLP (CPU)", - "penalty_l1" => string(penalty_l1), - "penalty_l2" => string(penalty_l2), - "penalty_schedule" => string(penalty_schedule), - "num_epochs" => string(num_epochs), - "num_batches" => string(num_batches), - "num_train_per_batch" => string(_num_train_per_batch), - "num_eval_scenarios" => num_eval_scenarios, - "eval_every" => eval_every, - "use_gpu" => USE_GPU, - ), -) - -# Define Model -num_uncertainties = length(uncertainty_samples[1][1]) -models = state_conditioned_policy( - num_uncertainties, - num_hydro, - num_hydro, - layers; - activation=activation, - encoder_type=Flux.LSTM, -) - -# Load pretrained Model -if !isnothing(pre_trained_model) - model_save = JLD2.load(pre_trained_model) - model_state = model_save["model_state"] - Flux.loadmodel!(models, model_state) -end - -# Initial evaluation -Random.seed!(8788) -@time objective_values = [ - simulate_multistage( - det_equivalent, - state_params_in, - state_params_out, - initial_state, - DecisionRules.sample(uncertainty_samples), - models; - ) for _ in 1:2 -] -best_obj = mean(objective_values) - -model_path = joinpath(model_dir, save_file * ".jld2") -save_control = SaveBest(best_obj, model_path) -stall_train = StallingCriterium(num_epochs * num_batches, best_obj, 0) -stall_rollout = StallingCriterium(num_epochs * num_batches, best_obj, 0) - - -# Rollout evaluation (stage-wise subproblems, CPU) -Random.seed!(8789) -eval_scenarios = [ - DecisionRules.sample(uncertainty_samples_sub) for _ in 1:num_eval_scenarios -] -rollout_evaluation = RolloutEvaluation( - subproblems, - state_params_in_sub, - state_params_out_sub, - initial_state, - eval_scenarios; - stride=eval_every, - policy_state=:target, -) -realized_rollout_evaluation = RolloutEvaluation( - subproblems, - state_params_in_sub, - state_params_out_sub, - initial_state, - eval_scenarios; - stride=eval_every, - policy_state=:realized, -) -resolved_penalty_schedule = isnothing(penalty_schedule) ? nothing : - DecisionRules._resolve_penalty_schedule(penalty_schedule, num_epochs * num_batches) - -# Train Model using deterministic equivalent. -train_multistage( - models, - initial_state, - det_equivalent, - state_params_in, - state_params_out, - uncertainty_samples; - num_batches=num_epochs * num_batches, - num_train_per_batch=_num_train_per_batch, - optimizer=first(optimizers), - record=(sample_log, iter, model) -> begin - training_loss = mean(sample_log.objectives) - loss_no_deficit = mean(sample_log.objectives_no_deficit) - metrics = Dict( - "metrics/loss" => loss_no_deficit, - "metrics/training_loss" => training_loss, - ) - rollout_evaluation(iter, model) - realized_rollout_evaluation(iter, model) - converged_training = stall_train(iter, model, training_loss) - converged_rollout = false - if iter % eval_every == 0 - converged_rollout = stall_rollout( - iter, model, rollout_evaluation.last_objective_no_deficit - ) - metrics["metrics/rollout_objective_no_deficit"] = - rollout_evaluation.last_objective_no_deficit - metrics["metrics/rollout_target_violation_share"] = - rollout_evaluation.last_violation_share - metrics["metrics/rollout_realized_objective_no_deficit"] = - realized_rollout_evaluation.last_objective_no_deficit - metrics["metrics/rollout_realized_target_violation_share"] = - realized_rollout_evaluation.last_violation_share - end - if !isnothing(resolved_penalty_schedule) - metrics["metrics/target_penalty_multiplier"] = - DecisionRules._penalty_multiplier_for(resolved_penalty_schedule, iter) - end - Wandb.log(lg, metrics) - save_control(iter, model, training_loss) - return converged_training && converged_rollout && isapprox(training_loss, rollout_evaluation.last_objective_no_deficit; rtol=0.01) - end, - penalty_schedule=penalty_schedule, -) - -# Finish the run -close(lg) diff --git a/examples/HydroPowerModels/train_dr_hydropowermodels_multipleshooting.jl b/examples/HydroPowerModels/train_dr_hydropowermodels_multipleshooting.jl deleted file mode 100644 index 906f8e4..0000000 --- a/examples/HydroPowerModels/train_dr_hydropowermodels_multipleshooting.jl +++ /dev/null @@ -1,208 +0,0 @@ -# Train HydroPowerModels using multiple shooting (windowed decomposition) -using DecisionRules -using Statistics -using Random -using Flux - -using Ipopt -using Wandb, Dates, Logging -using JLD2 -using DiffOpt - -HydroPowerModels_dir = dirname(@__FILE__) -include(joinpath(HydroPowerModels_dir, "load_hydropowermodels.jl")) - -# Functions - -function non_ensurance(x_out, x_in, uncertainty, max_volume) - return x_out -end - -# Parameters -case_name = "bolivia" # bolivia, case3 -formulation = "ACPPowerModel" # SOCWRConicPowerModel, DCPPowerModel, ACPPowerModel -num_stages = parse(Int, get(ENV, "DR_NUM_STAGES", "126")) -window_size = 12 # 12, 6 -model_dir = joinpath(HydroPowerModels_dir, case_name, formulation, "models") -mkpath(model_dir) -formulation_file = formulation * ".mof.json" - -# Training parameters -num_epochs = parse(Int, get(ENV, "DR_NUM_EPOCHS", "80")) -num_batches = 100 -_num_train_per_batch = 1 -activation = sigmoid # tanh, identity, relu, sigmoid -layers = Int64[128, 128] -ensure_feasibility = non_ensurance -grad_clip = parse(Float32, get(ENV, "DR_GRAD_CLIP", "0")) -optimizers = if grad_clip > 0 - [Flux.Optimisers.OptimiserChain(Flux.Optimisers.ClipGrad(grad_clip), Flux.Adam())] -else - [Flux.Adam()] -end -pre_trained_model = nothing -penalty_l2 = :auto -penalty_l1 = :auto -penalty_schedule = get(ENV, "DR_PENALTY_SCHEDULE", "annealed") == "annealed" ? :default_annealed : nothing -clip_tag = grad_clip > 0 ? "-clip$(Int(grad_clip))" : "" -sched_tag = isnothing(penalty_schedule) ? "-const" : "-anneal" -save_file = "$(case_name)-$(formulation)-h$(num_stages)-shooting-w$(window_size)$(clip_tag)$(sched_tag)-$(now())" -num_eval_scenarios = 4 -eval_every = 25 - -# Build MSP using subproblems (not deterministic equivalent) - -# Define the DiffOpt optimizer for subproblems and window models -diff_optimizer = - () -> DiffOpt.diff_optimizer( - optimizer_with_attributes( - Ipopt.Optimizer, - "print_level" => 0, - "linear_solver" => "mumps", - ), - ) - -diff_model = - () -> DiffOpt.nonlinear_diff_model( - optimizer_with_attributes( - Ipopt.Optimizer, - "print_level" => 0, - "linear_solver" => "mumps", - ), - ) - -subproblems, state_params_in, state_params_out, uncertainty_samples, initial_state, max_volume = build_hydropowermodels( - joinpath(HydroPowerModels_dir, case_name), - formulation_file; - num_stages=num_stages, - optimizer=diff_optimizer, - penalty_l1=penalty_l1, - penalty_l2=penalty_l2, -) - -num_hydro = length(initial_state) - -# Logging -lg = WandbLogger(; - project="RL", - name=save_file, - save_code=false, - config=Dict( - "layers" => layers, - "activation" => string(activation), - "ensure_feasibility" => string(ensure_feasibility), - "optimizer" => string(optimizers), - "grad_clip" => grad_clip, - "training_method" => "multiple_shooting", - "window_size" => string(window_size), - "penalty_l1" => string(penalty_l1), - "penalty_l2" => string(penalty_l2), - "penalty_schedule" => string(penalty_schedule), - "num_epochs" => string(num_epochs), - "num_batches" => string(num_batches), - "num_train_per_batch" => string(_num_train_per_batch), - ), -) - -# Define Model -# Policy architecture: LSTM processes uncertainty, Dense combines with previous state -num_uncertainties = length(uncertainty_samples[1][1]) -models = state_conditioned_policy( - num_uncertainties, - num_hydro, - num_hydro, - layers; - activation=activation, - encoder_type=Flux.LSTM, -) - -# Load pretrained Model -if !isnothing(pre_trained_model) - model_save = JLD2.load(pre_trained_model) - model_state = model_save["model_state"] - Flux.loadmodel!(models, model_state) -end - -# Initial evaluation -Random.seed!(8788) -windows = DecisionRules.setup_shooting_windows( - subproblems, - state_params_in, - state_params_out, - Float64.(initial_state), - uncertainty_samples; - window_size=window_size, - model_factory=diff_model, -) - -objective_values = [ - begin - uncertainty_sample = DecisionRules.sample(uncertainty_samples) - uncertainties_vec = [ - [Float32(u[2]) for u in stage_u] for stage_u in uncertainty_sample - ] - DecisionRules.simulate_multiple_shooting( - windows, models, Float32.(initial_state), uncertainty_sample, uncertainties_vec - ) - end for _ in 1:2 -] - -best_obj = mean(objective_values) - -model_path = joinpath(model_dir, save_file * ".jld2") -save_control = SaveBest(best_obj, model_path) -convergence_criterium = StallingCriterium(num_epochs * num_batches, best_obj, 0) - -Random.seed!(8789) -eval_scenarios = [DecisionRules.sample(uncertainty_samples) for _ in 1:num_eval_scenarios] -rollout_evaluation = RolloutEvaluation( - subproblems, - state_params_in, - state_params_out, - initial_state, - eval_scenarios; - stride=eval_every, - policy_state=:realized, -) -resolved_penalty_schedule = isnothing(penalty_schedule) ? nothing : - DecisionRules._resolve_penalty_schedule(penalty_schedule, num_epochs * num_batches) -pending_metrics = Dict{String,Any}() - -# Train Model using multiple shooting. -# A single call over num_epochs*num_batches batches so the penalty schedule spans the whole -# run (this also keeps one optimizer state throughout, and a `true` return from the record -# callback now stops the whole run). -train_multiple_shooting( - models, - initial_state, - windows, - uncertainty_samples; - num_batches=num_epochs * num_batches, - num_train_per_batch=_num_train_per_batch, - optimizer=first(optimizers), - record_loss=(iter, model, loss, tag) -> begin - pending_metrics[tag] = loss - if tag == "metrics/training_loss" - rollout_evaluation(iter, model) - if iter % eval_every == 0 - pending_metrics["metrics/rollout_objective_no_deficit"] = - rollout_evaluation.last_objective_no_deficit - pending_metrics["metrics/rollout_target_violation_share"] = - rollout_evaluation.last_violation_share - end - if !isnothing(resolved_penalty_schedule) - pending_metrics["metrics/target_penalty_multiplier"] = - DecisionRules._penalty_multiplier_for(resolved_penalty_schedule, iter) - end - Wandb.log(lg, copy(pending_metrics)) - empty!(pending_metrics) - save_control(iter, model, loss) - return convergence_criterium(iter, model, loss) - end - return false - end, - penalty_schedule=penalty_schedule, -) - -# Finish the run -close(lg) diff --git a/examples/HydroPowerModels/train_dr_hydropowermodels_subproblems.jl b/examples/HydroPowerModels/train_dr_hydropowermodels_subproblems.jl deleted file mode 100644 index 7428293..0000000 --- a/examples/HydroPowerModels/train_dr_hydropowermodels_subproblems.jl +++ /dev/null @@ -1,188 +0,0 @@ -# Train HydroPowerModels using stage-wise decomposition (single shooting, Extension §2) -using DecisionRules -using Statistics -using Random -using Flux - -using Ipopt -using Wandb, Dates, Logging -using JLD2 -using DiffOpt - -HydroPowerModels_dir = dirname(@__FILE__) -include(joinpath(HydroPowerModels_dir, "load_hydropowermodels.jl")) - -# Functions - -function non_ensurance(x_out, x_in, uncertainty, max_volume) - return x_out -end - -# Parameters -case_name = "bolivia" -formulation = "ACPPowerModel" -num_stages = parse(Int, get(ENV, "DR_NUM_STAGES", "126")) -model_dir = joinpath(HydroPowerModels_dir, case_name, formulation, "models") -mkpath(model_dir) -formulation_file = formulation * ".mof.json" -num_epochs = parse(Int, get(ENV, "DR_NUM_EPOCHS", "80")) -num_batches = 100 -_num_train_per_batch = 1 -activation = sigmoid -layers = Int64[128, 128] -ensure_feasibility = non_ensurance -grad_clip = parse(Float32, get(ENV, "DR_GRAD_CLIP", "0")) -optimizers = if grad_clip > 0 - [Flux.Optimisers.OptimiserChain(Flux.Optimisers.ClipGrad(grad_clip), Flux.Adam())] -else - [Flux.Adam()] -end -pre_trained_model = nothing -penalty_l2 = :auto -penalty_l1 = :auto -penalty_schedule = get(ENV, "DR_PENALTY_SCHEDULE", "constant") == "annealed" ? :default_annealed : nothing -clip_tag = grad_clip > 0 ? "-clip$(Int(grad_clip))" : "" -sched_tag = isnothing(penalty_schedule) ? "-const" : "-anneal" -save_file = "$(case_name)-$(formulation)-h$(num_stages)-subproblems$(clip_tag)$(sched_tag)-$(now())" -num_eval_scenarios = 4 # fixed held-out scenarios for the rollout evaluation -eval_every = 25 # rollout-evaluate every eval_every batches - -# Build MSP using subproblems (not deterministic equivalent) - -# Define the DiffOpt optimizer for subproblems -diff_optimizer = - () -> DiffOpt.diff_optimizer( - optimizer_with_attributes( - Ipopt.Optimizer, - "print_level" => 0, - "linear_solver" => "mumps", - ), - ) - -subproblems, state_params_in, state_params_out, uncertainty_samples, initial_state, max_volume = build_hydropowermodels( - joinpath(HydroPowerModels_dir, case_name), - formulation_file; - num_stages=num_stages, - optimizer=diff_optimizer, - penalty_l1=penalty_l1, - penalty_l2=penalty_l2, -) - -num_hydro = length(initial_state) - -# Logging - -lg = WandbLogger(; - project="RL", - name=save_file, - save_code=false, - config=Dict( - "layers" => layers, - "activation" => string(activation), - "ensure_feasibility" => string(ensure_feasibility), - "optimizer" => string(optimizers), - "grad_clip" => grad_clip, - "training_method" => "subproblems", - "penalty_l1" => string(penalty_l1), - "penalty_l2" => string(penalty_l2), - "penalty_schedule" => string(penalty_schedule), - "num_epochs" => string(num_epochs), - "num_batches" => string(num_batches), - "num_train_per_batch" => string(_num_train_per_batch), - ), -) - -# Define Model -# Policy architecture: LSTM processes uncertainty, Dense combines with previous state -num_uncertainties = length(uncertainty_samples[1][1]) -models = state_conditioned_policy( - num_uncertainties, - num_hydro, - num_hydro, - layers; - activation=activation, - encoder_type=Flux.LSTM, -) - -# Load pretrained Model -if !isnothing(pre_trained_model) - model_save = JLD2.load(pre_trained_model) - model_state = model_save["model_state"] - Flux.loadmodel!(models, model_state) -end - -Random.seed!(8788) -objective_values = [ - simulate_multistage( - subproblems, - state_params_in, - state_params_out, - initial_state, - DecisionRules.sample(uncertainty_samples), - models; - ) for _ in 1:2 -] -best_obj = mean(objective_values) - -model_path = joinpath(model_dir, save_file * ".jld2") -save_control = SaveBest(best_obj, model_path) -convergence_criterium = StallingCriterium(num_epochs * num_batches, best_obj, 0) - -# Fixed held-out scenarios, materialized once so every evaluation uses the same set. -# The rollout evaluation executes the policy stage by stage (deployment semantics) and -# reports the operational cost (objective excluding the target-slack penalty) plus the -# target-violation share; comparisons are only trustworthy when the share is <= ~0.05. -Random.seed!(8789) -eval_scenarios = [DecisionRules.sample(uncertainty_samples) for _ in 1:num_eval_scenarios] -rollout_evaluation = RolloutEvaluation( - subproblems, - state_params_in, - state_params_out, - initial_state, - eval_scenarios; - stride=eval_every, - policy_state=:realized, -) -resolved_penalty_schedule = isnothing(penalty_schedule) ? nothing : - DecisionRules._resolve_penalty_schedule(penalty_schedule, num_epochs * num_batches) - -# Train Model using subproblems (not deterministic equivalent). -# A single call over num_epochs*num_batches batches so the penalty schedule spans the whole -# run (this also keeps one optimizer state throughout, and a `true` return from the record -# callback now stops the whole run). -train_multistage( - models, - initial_state, - subproblems, - state_params_in, - state_params_out, - uncertainty_samples; - num_batches=num_epochs * num_batches, - num_train_per_batch=_num_train_per_batch, - optimizer=first(optimizers), - record=(sample_log, iter, model) -> begin - training_loss = mean(sample_log.objectives) - metrics = Dict( - "metrics/loss" => mean(sample_log.objectives_no_deficit), - "metrics/training_loss" => training_loss, - ) - rollout_evaluation(iter, model) - if iter % eval_every == 0 - metrics["metrics/rollout_objective_no_deficit"] = - rollout_evaluation.last_objective_no_deficit - metrics["metrics/rollout_target_violation_share"] = - rollout_evaluation.last_violation_share - end - if !isnothing(resolved_penalty_schedule) - metrics["metrics/target_penalty_multiplier"] = - DecisionRules._penalty_multiplier_for(resolved_penalty_schedule, iter) - end - Wandb.log(lg, metrics) - save_control(iter, model, training_loss) - return convergence_criterium(iter, model, training_loss) - end, - penalty_schedule=penalty_schedule, -) - -# Finish the run -close(lg) diff --git a/examples/HydroPowerModels/train_dr_l2O_supervised.jl b/examples/HydroPowerModels/train_dr_l2O_supervised.jl deleted file mode 100644 index 5d9466c..0000000 --- a/examples/HydroPowerModels/train_dr_l2O_supervised.jl +++ /dev/null @@ -1,206 +0,0 @@ -using CUDA -using Wandb, Dates, Logging -using Arrow - -using JLD2 -using Statistics -using Random -using Flux -using JSON -using DataFrames -using DecisionRules - -# CUDA.set_runtime_version!(v"12.1.0") - -case_name = "case3" -formulation = "ACPPowerModel" -num_stages = 48 -batch_size = 32 -num_epochs = 10 -optimizer = Flux.RMSProp() -os = cpu # cpu, gpu - -save_file = "supervised-$(case_name)-$(formulation)-h$(num_stages)-$(now())" - -HydroPowerModels_dir = dirname(@__FILE__) -case_dir = joinpath(HydroPowerModels_dir, case_name) -model_dir = joinpath(case_dir, formulation, "models") -output_dir = joinpath(case_dir, formulation, "output") - -hydro_file = JSON.parsefile(joinpath(case_dir, "hydro.json")) - -num_hydro = length(hydro_file["Hydrogenerators"]) -stage_hours = hydro_file["stage_hours"] -volume_to_mw(volume, stage_hours; k=0.0036) = volume / (k * stage_hours) - -input_files = [ - file for file in readdir(case_dir; join=true) if ( - occursin(case_name, file) && - occursin(formulation, file) && - occursin("arrow", file) && - occursin("input", file) - ) -] - -output_files = [ - file for file in readdir(output_dir; join=true) if ( - occursin(case_name, file) && - occursin(formulation, file) && - occursin("arrow", file) && - occursin("output", file) - ) -] - -input_table = deepcopy(DataFrame(Arrow.Table(input_files))) -output_table = DataFrame(Arrow.Table(output_files)) - -for i in 1:num_hydro - input_table[:, Symbol("_inflow[$i]#1")] = - input_table[:, Symbol("_inflow[$i]#1")] .+ - volume_to_mw.(input_table[:, Symbol("_reservoir[$i]_in#1")], stage_hours) -end - -input_names = [[Symbol("_inflow[$i]#$t") for i in 1:num_hydro] for t in 1:num_stages] -output_names = [[Symbol("reservoir[$i]_out#$t") for i in 1:num_hydro] for t in 1:num_stages] - -output_table[!, vcat(output_names...)] .= - sum(Matrix(output_table[!, vcat(output_names...)]); dims=1) ./ size(output_table, 1) - -data_table = innerjoin(input_table, output_table; on=:id) -data_table = data_table -for in_name in vcat(input_names...) - data_table[!, in_name] = Vector(data_table[:, in_name]) -end -for out_name in vcat(output_names...) - data_table[!, out_name] = Vector(data_table[:, out_name]) -end - -model = os(Chain(Dense(num_hydro, 8, relu), LSTM(8, 8), Dense(8, num_hydro))) - -function train_test( - model, - data_table, - num_hydro, - num_stages, - input_names, - output_names; - loss=Flux.mse, - batch_size=32, - optimizer=Flux.Adam(0.01), - os=cpu, - record_loss=(iter, model, loss, tag) -> begin - println("tag: $tag, Iter: $iter, Loss: $loss") - return false - end, -) - num_samples = length(data_table.id) - num_batches = ceil(Int, num_samples / batch_size) - - # Initialise the optimiser for this model: - opt_state = Flux.setup(optimizer, model) - - for iter in 1:num_batches - iter_data_table = data_table[ - ((iter - 1) * batch_size + 1):min(iter * batch_size, num_samples), :, - ] - in_data = [ - [ - os([iter_data_table[s, input_names[t][i]] for i in 1:num_hydro]) for - s in 1:length(iter_data_table.id) - ] for t in 1:num_stages - ] - out_data = [ - [ - os([iter_data_table[s, output_names[t][i]] for i in 1:num_hydro]) for - s in 1:length(iter_data_table.id) - ] for t in 1:num_stages - ] - objective = 0.0 - grads = Flux.gradient(model) do m - for s in 1:length(iter_data_table.id) - Flux.reset!(m) - target_states = hcat([m(in_data[t][s]) for t in 1:num_stages]...) - optimal_states = hcat([out_data[t][s] for t in 1:num_stages]...) - objective += loss(target_states, optimal_states) - end - objective /= batch_size - return objective - end - record_loss(iter, model, objective, "metrics/batch_loss") && break - - # Update the parameters so as to reduce the objective, - # according the chosen optimisation rule: - Flux.update!(opt_state, model, grads[1]) - end -end - -model_path = joinpath(model_dir, save_file * ".jld2") - -save_control = SaveBest(100, model_path) - -lg = WandbLogger(; - project="HydroPowerModels", - name=save_file, - save_code=false, - config=Dict("Supervised" => "Yes", "optimizer" => "Adam"), -) - -function record_loss(iter, model, loss, tag) - Wandb.log(lg, Dict(tag => loss)) - return false -end - -function train_multi_epoch( - model, - data_table, - num_hydro, - num_stages, - input_names, - output_names; - loss=Flux.mse, - batch_size=32, - optimizer=Flux.Adam(0.01), - num_epochs=1, - os=cpu, - record_loss=(iter, model, loss, tag) -> begin - println("tag: $tag, Iter: $iter, Loss: $loss") - return false - end, -) - for epoch in 1:num_epochs - data_table = data_table[shuffle(1:size(data_table, 1)), :] - train_test( - model, - data_table, - num_hydro, - num_stages, - input_names, - output_names; - record_loss=record_loss, - optimizer=optimizer, - batch_size=batch_size, - loss=loss, - os=os, - ) - end -end - -train_multi_epoch( - model, - data_table, - num_hydro, - num_stages, - input_names, - output_names; - num_epochs=num_epochs, - optimizer=optimizer, - batch_size=batch_size, - os=os, - record_loss=(iter, model, loss, tag) -> begin - save_control(iter, model, loss) - return record_loss(iter, model, loss, tag) - end, -) - -# Finish the run -close(lg) diff --git a/examples/HydroPowerModels/train_ldr_hydropowermodels.jl b/examples/HydroPowerModels/train_ldr_hydropowermodels.jl deleted file mode 100644 index c296ce2..0000000 --- a/examples/HydroPowerModels/train_ldr_hydropowermodels.jl +++ /dev/null @@ -1,264 +0,0 @@ -# Train a TS-LDR (Linear Decision Rule) policy on the Bolivia LTHD problem. -# -# TS-LDR uses the same target-setting framework as TS-DDR but replaces the -# deep neural network with a linear map: -# -# x̂_t = W [w_{1:t}; x_{t-1}] + b -# -# where W, b are the trainable parameters. This is a `dense_multilayer_nn` -# with identity activation — a composition of linear layers is still linear, -# so the result is a standard linear decision rule. -# -# Training uses the Deterministic Equivalent pipeline (all stages coupled in -# one NLP), identical to train_dr_hydropowermodels.jl except for the policy -# architecture. The saved model is evaluated by evaluate_hydro_policies.jl. -# -# Usage: -# julia --project=. train_ldr_hydropowermodels.jl - -using DecisionRules -using Statistics -using Random -using Flux - -using Ipopt -using Wandb, Dates, Logging -using JLD2 -using DiffOpt -using JuMP -using MadNLP - -USE_GPU = try - using CUDA, CUDSS, MadNLPGPU - CUDA.functional() -catch - @warn "GPU packages not available — running on CPU" - false -end -@info "GPU status" USE_GPU - -HydroPowerModels_dir = dirname(@__FILE__) -include(joinpath(HydroPowerModels_dir, "load_hydropowermodels.jl")) - -function non_ensurance(x_out, x_in, uncertainty, max_volume) - return x_out -end - -# ── Parameters ─────────────────────────────────────────────────────────────── - -case_name = "bolivia" -formulation = "ACPPowerModel" -num_stages = 96 -model_dir = joinpath(HydroPowerModels_dir, case_name, formulation, "models") -mkpath(model_dir) -solver_tag = USE_GPU ? "gpu" : "cpu" -save_file = "$(case_name)-$(formulation)-h$(num_stages)-ldr-$(solver_tag)-$(now())" -formulation_file = formulation * ".mof.json" - -num_epochs = 40 -num_batches = 100 -_num_train_per_batch = 1 -activation = identity -layers = Int64[64, 64] -ensure_feasibility = non_ensurance -optimizers = [Flux.Adam()] -pre_trained_model = nothing -penalty_l2 = :auto -penalty_l1 = :auto -penalty_schedule = [ - (1, 100, 0.1), - (101, 210, 1.0), - (211, 300, 10.0), - (301, num_epochs * num_batches, 30.0), -] -num_eval_scenarios = 4 -eval_every = 25 - -# ── Build MSP: subproblems for rollout evaluation ──────────────────────────── - -diff_optimizer = - () -> DiffOpt.diff_optimizer( - optimizer_with_attributes( - Ipopt.Optimizer, - "print_level" => 0, - "linear_solver" => "mumps", - ), - ) -subproblems, state_params_in_sub, state_params_out_sub, uncertainty_samples_sub, initial_state, max_volume = build_hydropowermodels( - joinpath(HydroPowerModels_dir, case_name), - formulation_file; - num_stages=num_stages, - optimizer=diff_optimizer, - penalty_l1=penalty_l1, - penalty_l2=penalty_l2, -) - -# ── Build det-eq for training ──────────────────────────────────────────────── - -subproblems_de, state_params_in, state_params_out, uncertainty_samples, _, _ = build_hydropowermodels( - joinpath(HydroPowerModels_dir, case_name), - formulation_file; - num_stages=num_stages, - penalty_l1=penalty_l1, - penalty_l2=penalty_l2, -) - -det_equivalent = Model(MadNLP.Optimizer) - -if USE_GPU - set_optimizer_attribute(det_equivalent, "array_type", CUDA.CuArray) - set_optimizer_attribute(det_equivalent, "linear_solver", MadNLPGPU.CUDSSSolver) - set_optimizer_attribute(det_equivalent, "print_level", MadNLP.ERROR) - set_optimizer_attribute(det_equivalent, "barrier", MadNLP.LOQOUpdate()) -else - set_optimizer_attribute(det_equivalent, "print_level", MadNLP.ERROR) - set_optimizer_attribute(det_equivalent, "barrier", MadNLP.LOQOUpdate()) -end - -det_equivalent, uncertainty_samples = DecisionRules.deterministic_equivalent!( - det_equivalent, - subproblems_de, - state_params_in, - state_params_out, - initial_state, - uncertainty_samples, -) - -num_hydro = length(initial_state) - -# ── Logging ────────────────────────────────────────────────────────────────── - -lg = WandbLogger(; - project="RL", - name=save_file, - save_code=false, - config=Dict( - "layers" => layers, - "activation" => "identity (LDR)", - "policy_type" => "dense_multilayer_nn", - "ensure_feasibility" => string(ensure_feasibility), - "optimizer" => string(optimizers), - "training_method" => "deterministic_equivalent", - "solver" => USE_GPU ? "MadNLP+CUDSS (GPU)" : "MadNLP (CPU)", - "penalty_l1" => string(penalty_l1), - "penalty_l2" => string(penalty_l2), - "penalty_schedule" => string(penalty_schedule), - "num_epochs" => string(num_epochs), - "num_batches" => string(num_batches), - "num_train_per_batch" => string(_num_train_per_batch), - "num_eval_scenarios" => num_eval_scenarios, - "eval_every" => eval_every, - "use_gpu" => USE_GPU, - ), -) - -# ── Define linear policy ───────────────────────────────────────────────────── - -num_uncertainties = length(uncertainty_samples[1][1]) -num_inputs = DecisionRules.policy_input_dim(num_uncertainties, num_hydro) -models = dense_multilayer_nn( - num_inputs, num_hydro, layers; - activation=activation, -) - -if !isnothing(pre_trained_model) - model_save = JLD2.load(pre_trained_model) - model_state = model_save["model_state"] - Flux.loadmodel!(models, model_state) -end - -# ── Initial evaluation ─────────────────────────────────────────────────────── - -Random.seed!(8788) -@time objective_values = [ - simulate_multistage( - det_equivalent, - state_params_in, - state_params_out, - initial_state, - DecisionRules.sample(uncertainty_samples), - models; - ) for _ in 1:2 -] -best_obj = mean(objective_values) - -model_path = joinpath(model_dir, save_file * ".jld2") -save_control = SaveBest(best_obj, model_path) -stall_train = StallingCriterium(100, best_obj, 0) -stall_rollout = StallingCriterium(5, best_obj, 0) - -# ── Rollout evaluation (stage-wise subproblems, CPU) ───────────────────────── - -Random.seed!(8789) -eval_scenarios = [ - DecisionRules.sample(uncertainty_samples_sub) for _ in 1:num_eval_scenarios -] -rollout_evaluation = RolloutEvaluation( - subproblems, - state_params_in_sub, - state_params_out_sub, - initial_state, - eval_scenarios; - stride=eval_every, - policy_state=:target, -) -realized_rollout_evaluation = RolloutEvaluation( - subproblems, - state_params_in_sub, - state_params_out_sub, - initial_state, - eval_scenarios; - stride=eval_every, - policy_state=:realized, -) -resolved_penalty_schedule = isnothing(penalty_schedule) ? nothing : - DecisionRules._resolve_penalty_schedule(penalty_schedule, num_epochs * num_batches) - -# ── Train ──────────────────────────────────────────────────────────────────── - -train_multistage( - models, - initial_state, - det_equivalent, - state_params_in, - state_params_out, - uncertainty_samples; - num_batches=num_epochs * num_batches, - num_train_per_batch=_num_train_per_batch, - optimizer=first(optimizers), - record=(sample_log, iter, model) -> begin - training_loss = mean(sample_log.objectives) - loss_no_deficit = mean(sample_log.objectives_no_deficit) - metrics = Dict( - "metrics/loss" => loss_no_deficit, - "metrics/training_loss" => training_loss, - ) - rollout_evaluation(iter, model) - realized_rollout_evaluation(iter, model) - converged_training = stall_train(iter, model, training_loss) - converged_rollout = false - if iter % eval_every == 0 - converged_rollout = stall_rollout( - iter, model, rollout_evaluation.last_objective_no_deficit - ) - metrics["metrics/rollout_objective_no_deficit"] = - rollout_evaluation.last_objective_no_deficit - metrics["metrics/rollout_target_violation_share"] = - rollout_evaluation.last_violation_share - metrics["metrics/rollout_realized_objective_no_deficit"] = - realized_rollout_evaluation.last_objective_no_deficit - metrics["metrics/rollout_realized_target_violation_share"] = - realized_rollout_evaluation.last_violation_share - end - if !isnothing(resolved_penalty_schedule) - metrics["metrics/target_penalty_multiplier"] = - DecisionRules._penalty_multiplier_for(resolved_penalty_schedule, iter) - end - Wandb.log(lg, metrics) - save_control(iter, model, training_loss) - return converged_training && converged_rollout && isapprox(training_loss, rollout_evaluation.last_objective_no_deficit; rtol=0.01) - end, - penalty_schedule=penalty_schedule, -) - -close(lg) diff --git a/examples/README.md b/examples/README.md index 23e4d16..c0a3468 100644 --- a/examples/README.md +++ b/examples/README.md @@ -8,10 +8,8 @@ and additional experiments. | Directory | Application | Paper section | |-----------|------------|---------------| -| [`HydroPowerModels/`](HydroPowerModels/) | Bolivia Long-Term Hydrothermal Dispatching (10 hydro units, AC/SOC/DC OPF, 96 stages). Trains TS-DDR (LSTM) and TS-LDR (linear) policies. | §4, Extension §1–§4 | | [`inventory_control/`](inventory_control/) | Stochastic lot-sizing with fixed ordering costs (relaxed LP and integer MIP). Demonstrates score-function (REINFORCE) gradient mixing for integer variables. | §3 | | [`rocket_control/`](rocket_control/) | Goddard rocket altitude maximization with stochastic wind | §3 | -| [`RL/`](RL/) | Reinforcement learning baselines (REINFORCE, PPO, DDPG, TD3, SAC) on Bolivia LTHD | Beyond paper | | `Experimental/` | Work-in-progress experiments (not documented) | — | ## Utility scripts @@ -19,7 +17,6 @@ and additional experiments. | Script | Description | |--------|-------------| | `slurm.jl` | SLURM launcher: starts Distributed workers via `ClusterManagers` and includes a target script | -| `solve_dataset.jl` | Distributed batch solver for L2O dataset generation (uses [L2O.jl](https://github.com/andrewrosemberg/L2O.jl)) | ## Quick start @@ -28,24 +25,10 @@ before running: ```julia using Pkg -Pkg.activate("examples/HydroPowerModels") +Pkg.activate("examples/inventory_control") Pkg.instantiate() -include("examples/HydroPowerModels/train_dr_hydropowermodels_subproblems.jl") +include("examples/inventory_control/train_dr_inventory.jl") ``` For GPU-accelerated training on SLURM, see the `.sbatch` files in each subdirectory. - -## Training methods compared - -All three decomposition strategies from the paper can be trained on the -same problem: - -1. **Deterministic Equivalent** — single coupled NLP over all stages (Extension §1) -2. **Stage-wise / Single Shooting** — solve one subproblem per stage, backpropagate through the chain (Extension §2) -3. **Windowed / Multiple Shooting** — partition stages into windows, parallelize window solves (Extension §3) - -The HydroPowerModels directory contains a training script for each strategy, -a TS-LDR training script (linear policy baseline), and an evaluation script -(`evaluate_hydro_policies.jl`) that runs all trained policies on a common -out-of-sample scenario set. diff --git a/examples/RL/Project.toml b/examples/RL/Project.toml deleted file mode 100644 index b814d81..0000000 --- a/examples/RL/Project.toml +++ /dev/null @@ -1,23 +0,0 @@ -[deps] -BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0" -CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" -ClusterManagers = "34f1f09b-3a8b-5176-ab39-66d58a4d544e" -CommonRLInterface = "d842c3ba-07a1-494f-bbec-f5741b0a3e98" -CommonRLSpaces = "408f5b3e-f2a2-48a6-b4bb-c8aa44c458e6" -Crux = "e51cc422-768a-4345-bb8e-2246287ae729" -DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" -DecisionRules = "47937410-f832-486f-8300-12c95b225dfc" -Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" -FFMPEG = "c87230d0-a227-11e9-1b43-d7ebe4e7570a" -Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" -HSL_jll = "017b0a0e-03f4-516a-9b91-836bbd1904dd" -JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" -JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" -POMDPTools = "7588e00f-9cae-40de-98dc-e0c70c48cdd7" -POMDPs = "a93abf59-7444-517b-a68a-c42f96afdd7d" -Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -QuickPOMDPs = "8af83fb2-a731-493c-9049-9e19dbce6165" -Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" -Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" -Wandb = "ad70616a-06c9-5745-b1f1-6a5f42545108" -cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" diff --git a/examples/RL/README.md b/examples/RL/README.md deleted file mode 100644 index de44161..0000000 --- a/examples/RL/README.md +++ /dev/null @@ -1,40 +0,0 @@ -# Reinforcement Learning Baselines - -This directory benchmarks TS-DDR against standard deep reinforcement -learning algorithms (REINFORCE, PPO, DDPG, TD3, SAC) on the Bolivia -hydrothermal dispatching problem, using [Crux.jl](https://github.com/ancorso/Crux.jl) -for RL training. - -## Problem overview - -The Bolivia LTHD problem is cast as a POMDP: the agent observes rainfall -(partial observability), selects reservoir-level actions, and receives -negative dispatch cost as reward. The environment wraps DecisionRules -stage-wise subproblems, so each step solves a full OPF. - -## Scripts - -| Script | Description | -|--------|-------------| -| `hydropowermodels_rl.jl` | Train RL agents (REINFORCE, PPO, DDPG, TD3, SAC) from scratch on Bolivia LTHD and plot learning curves | -| `hydro_pre_trained.jl` | Fine-tune RL agents from a TS-DDR pre-trained policy (warm-start the actor from a saved JLD2 model) | -| `test.jl` | Quick sanity check of the MDP construction | - -## Benchmark results - -Pre-generated learning curves are saved as: -- `hydro_benchmark.pdf` — RL from scratch -- `hydro_benchmark_warm.pdf` — RL fine-tuned from TS-DDR - -## Running - -Requires Distributed workers (uses `@everywhere`). Launch with: - -```bash -julia -p 4 examples/RL/hydropowermodels_rl.jl -``` - -## Dependencies - -See `Project.toml`. Key additional packages beyond DecisionRules: Crux, -POMDPs, POMDPTools, CommonRLInterface, Distributions. diff --git a/examples/RL/hydro_benchmark.pdf b/examples/RL/hydro_benchmark.pdf deleted file mode 100644 index 31f17d9..0000000 Binary files a/examples/RL/hydro_benchmark.pdf and /dev/null differ diff --git a/examples/RL/hydro_benchmark_warm.pdf b/examples/RL/hydro_benchmark_warm.pdf deleted file mode 100644 index 0637a8c..0000000 Binary files a/examples/RL/hydro_benchmark_warm.pdf and /dev/null differ diff --git a/examples/RL/hydro_pre_trained.jl b/examples/RL/hydro_pre_trained.jl deleted file mode 100644 index 56cd5bb..0000000 --- a/examples/RL/hydro_pre_trained.jl +++ /dev/null @@ -1,331 +0,0 @@ -using Distributed -using Random - -@everywhere rl_path = @__DIR__ -@everywhere dl_path = dirname(dirname(rl_path)) - -@everywhere import Pkg -@everywhere Pkg.activate(dl_path) -@everywhere Pkg.instantiate() -@everywhere using DecisionRules - -@everywhere Pkg.activate(rl_path) -@everywhere Pkg.instantiate() - -@everywhere import QuickPOMDPs: QuickPOMDP -@everywhere import POMDPTools: ImplicitDistribution, Deterministic, FunctionPolicy -@everywhere import Distributions: Normal, Uniform -@everywhere using POMDPs -@everywhere using Flux -@everywhere using Crux -@everywhere import Crux: state_space -@everywhere using Random -@everywhere using Distributions -@everywhere using CommonRLInterface -@everywhere using CommonRLSpaces - -@everywhere using Ipopt, HSL_jll -@everywhere HydroPowerModels_dir = joinpath(dirname(rl_path), "HydroPowerModels") -@everywhere include(joinpath(HydroPowerModels_dir, "load_hydropowermodels.jl")) - -@everywhere case_name = "bolivia" # bolivia, case3 -@everywhere formulation = "ACPPowerModel" # SOCWRConicPowerModel, DCPPowerModel, ACPPowerModel -@everywhere num_stages = 96 # 96, 48 - -import Crux: new_ep_reset! -Crux.new_ep_reset!(π::ContinuousNetwork) = begin - Flux.reset!(π.network) -end - -@everywhere function build_mdp( - case_name, - formulation, - num_stages; - solver=optimizer_with_attributes( - Ipopt.Optimizer, - "print_level" => 0, - "hsllib" => HSL_jll.libhsl_path, - "linear_solver" => "ma27", - ), - penalty=1e6, - _objective_value=objective_value, -) - formulation_file = formulation * ".mof.json" - Random.seed!(1234) - subproblems, state_params_in, state_params_out, uncertainty_samples, initial_state, max_volume = build_hydropowermodels( - joinpath(HydroPowerModels_dir, case_name), - formulation_file; - num_stages=num_stages, - penalty=penalty, - ) - - for subproblem in subproblems - set_optimizer(subproblem, solver) - end - - # test solve - JuMP.optimize!(subproblems[1]) - termination_status(subproblems[1]) - - Random.seed!(1234) - uncertainty_sample = DecisionRules.sample(uncertainty_samples)[1] - rain_state = [uncertainty_sample[i][2] for i in 1:length(uncertainty_sample)] - - num_a = length(state_params_in[1]) - mdp = QuickPOMDP( - actions=Box(zeros(num_a), max_volume), - obstype=Array{Float64,1}, - gen=function (state, state_out, rng) - state_in, rain_state, j = state[1:num_a], - state[(num_a + 1):(end - 1)], - ceil(Int, state[end]) - rain = if j == num_stages - DecisionRules.sample(uncertainty_samples[j]) - else - DecisionRules.sample(uncertainty_samples[j + 1]) - end - rain = [rain[i][2] for i in 1:length(rain)] - uncertainty_sample = [ - (uncertainty_samples[j][i][1], rain_state[i]) for i in 1:length(rain) - ] - simulate_stage( - subproblems[j], - state_params_in[j], - state_params_out[j], - uncertainty_sample, - state_in, - state_out, - ) - r = _objective_value(subproblems[j]) - next_volume = DecisionRules.get_next_state( - subproblems[j], state_params_in[j], state_params_out[j], state_in, state_out - ) - @info "Stage t=$j" sum(state_in) sum(rain_state) sum(state_out) sum( - next_volume - ) r - sp = [next_volume; rain; j+1] - # Partial observability: policy only sees rainfall, not full state - o = rain - return (sp=sp, o=o, r=(-r)) - end, - initialstate=Deterministic([initial_state; rain_state; 1.0]), - initialobs=(s) -> Deterministic(rain_state), - isterminal=s -> s[end] > num_stages, - ) - return mdp, num_a, max_volume -end - -# build the MDPs -@everywhere mdp, num_a, max_volume = build_mdp( - case_name, - formulation, - num_stages; - _objective_value=DecisionRules.get_objective_no_target_deficit, -) -@everywhere S = state_space(mdp) - -# Load TS-GDR model -using JLD2 -dense = LSTM -activation = sigmoid -layers = Int64[32, 32] -model = dense_multilayer_nn(1, num_a, num_a, layers; activation=activation, dense=dense) -model_dir = joinpath(HydroPowerModels_dir, case_name, "DCPPowerModel", "models") -model_file = readdir(model_dir; join=true)[end - 1] # edit this for a specific model -opt_state = Flux.setup(Flux.Adam(0.01), model) -x = randn(num_a, 1) -y = rand(num_a, 1) -train_set = [(x, y)] -Flux.train!(model, train_set, opt_state) do m, x, y - return Flux.mse(m(x), y) -end -models = model -model_state = JLD2.load(model_file, "model_state") -Flux.loadmodel!(model, model_state) - -# Sample expert data -Random.seed!(8788) -s = Sampler(mdp, ContinuousNetwork(model, num_a); max_steps=96, required_columns=[:t]) -data = steps!(s; Nsteps=97) - -####################### Imitation Learning ####################### -# Save the expert data -data[:expert_val] = ones(Float32, 1, 10000) -data = ExperienceBuffer(data) -BSON.@save "./expert.bson" data - -# Load the expert data and train the policy using Behavioral Cloning -expert_trajectories = BSON.load("./expert.bson")[:data] - -𝒮_bc = BC(; - π=ActorCritic(SG(), DoubleNetwork(QSA(), QSA())), #ActorCritic(SG(), V()), - 𝒟_demo=expert_trajectories, - S=S, - opt=(epochs=10000, batch_size=1024), - log=(period=500,), - max_steps=1000, -) -solve(𝒮_bc, mdp) - -####################### The rest of this code is exploratory ####################### -# This code is used to refine the policy with RL algorithms -# These policies only observe the uncertainty and not the rest of the state -#################################################################################### - -# Define the networks we will use -# @everywhere QSA() = ContinuousNetwork(Chain(Dense(34, 64, relu), Dense(64, 64, relu), Dense(64, 1))) -# @everywhere V() = ContinuousNetwork(Chain(Dense(2*num_a+1, 64, relu), Dense(64, 64, relu), Dense(64, 1))) -# @everywhere A() = ContinuousNetwork(Chain(Dense(2*num_a+1, 64, relu), Dense(64, 64, relu), Dense(64, num_a, tanh))) -# @everywhere SG() = SquashedGaussianPolicy(ContinuousNetwork(Chain(Dense(2*num_a+1, 64, relu), Dense(64, 64, relu), Dense(64, num_a, tanh))), zeros(Float32, 1), 1f0) - -# Solve with REINFORCE -@everywhere 𝒮_reinforce = REINFORCE( - π=GaussianPolicy(ContinuousNetwork(model, num_a), zeros(Float32, 1)), - S=S, - N=2000, - ΔN=10, - a_opt=(batch_size=1,), -) -@time π_reinforce = solve(𝒮_reinforce, mdp) - -# Solve with PPO -@everywhere 𝒮_ppo = PPO( - π=ActorCritic(GaussianPolicy(ContinuousNetwork(model, num_a), zeros(Float32, 1)), V()), - S=S, - N=10000, - ΔN=10, - a_opt=(batch_size=10,), -) -@time π_ppo = solve(𝒮_ppo, mdp) - -# Off-policy settings -@everywhere off_policy = ( - S=S, - ΔN=10, - N=1000, - buffer_size=Int(200), - buffer_init=200, - c_opt=(batch_size=1, optimizer=Adam(1e-3)), - a_opt=(batch_size=1, optimizer=Adam(1e-3)), - π_explore=GaussianNoiseExplorationPolicy(0.5f0, a_min=[0.0], a_max=[1.0]), -) - -# # Solver with DDPG -@everywhere QSA() = - ContinuousNetwork(Chain(Dense(22, 64, relu), Dense(64, 64, relu), Dense(64, 1))) -@everywhere 𝒮_ddpg = DDPG(; - π=ActorCritic(ContinuousNetwork(model, num_a), QSA()), off_policy... -) -@time π_ddpg = solve(𝒮_ddpg, mdp) - -# # Solve with TD3 -# @everywhere 𝒮_td3 = TD3(;π=ActorCritic(A(), DoubleNetwork(QSA(), QSA())), off_policy...) -# @time π_td3 = solve(𝒮_td3, mdp) - -# # Solve with SAC -@everywhere 𝒮_sac = SAC(; - π=ActorCritic( - GaussianPolicy(ContinuousNetwork(model, num_a), zeros(Float32, 1)), - DoubleNetwork(QSA(), QSA()), - ), - off_policy..., -) -@time π_sac = solve(𝒮_sac, mdp) - -# pmap(solve, [𝒮_reinforce, 𝒮_ppo, 𝒮_ddpg, 𝒮_td3, 𝒮_sac], [build_mdp(case_name, formulation, num_stages)[1] for _ in 1:5]) - -####################### This code is for Plotting ####################### - -using Plots -function plot_learning_mod( - input; - title="", - ylabel="Undiscounted Return (Costs)", - xlabel="Training Steps", - values=:undiscounted_return, - labels=:default, - legend=:bottomright, - font=:palatino, - p=plot(), - vertical_lines=[], - vline_range=(0, 1), - thick_every=1, - yscale=:log10, - xs=nothing, -) - dirs = directories(input) - - N = length(dirs) - values isa Symbol && (values = fill(values, N)) - if labels == :default - labels = N == 1 ? [""] : ["Task $i" for i in 1:N] - end - - # Plot the vertical lines (usually for multitask learning or to designate a point on the curve) - for i in 1:length(vertical_lines) - plot!( - p, - [vertical_lines[i], vertical_lines[i]], - [vline_range...]; - color=:black, - linewidth=i % thick_every == 0 ? 3 : 1, - label="", - ) - end - - # Plot the learning curves - plot!( - p; - ylabel=ylabel, - xlabel=xlabel, - legend=legend, - title=title, - fontfamily=font, - yscale=yscale, - ) - for i in 1:length(dirs) - x, y = readtb(dirs[i], values[i]) - if xs != nothing - x = x[xs] - y = y[xs] - end - plot!(p, x, -y; alpha=0.3, color=i, label="", yscale=yscale) - plot!(p, x, smooth(-y); color=i, label=labels[i], linewidth=2, yscale=yscale) - end - return p -end - -function Base.push!( - history::Crux.ValueHistories.History{I,V}, iteration::I, value::V -) where {I,V} - lastiter = history.lastiter - # iteration > lastiter || throw(ArgumentError("Iterations must increase over time")) - history.lastiter = iteration - push!(history.iterations, iteration) - push!(history.values, value) - return value -end - -p = plot_learning_mod( - [𝒮_reinforce, 𝒮_ppo, 𝒮_ddpg, 𝒮_td3, 𝒮_sac, 𝒮_bc]; - title="LTHD Training Curves", - labels=["REINFORCE", "PPO", "DDPG", "TD3", "SAC", "BC"], - legend=:outertopright, - yscale=:log10, -) - -using CSV, DataFrames -tsgdr_df = CSV.read("../../tsgdr_train.csv", DataFrame) - -plot!( - p, - tsgdr_df[:, "Step"], - tsgdr_df[ - :, "bolivia-ACPPowerModel-h96-2024-05-18T17:06:09.485 - metrics/training_loss" - ]; - label="TS-GDR", - linewidth=2, - yscale=:log10, -) - -Crux.savefig("./hydro_benchmark_warm.pdf") diff --git a/examples/RL/hydropowermodels_rl.jl b/examples/RL/hydropowermodels_rl.jl deleted file mode 100644 index 6e49a86..0000000 --- a/examples/RL/hydropowermodels_rl.jl +++ /dev/null @@ -1,256 +0,0 @@ -using Distributed -using Random - -@everywhere rl_path = @__DIR__ -@everywhere dl_path = dirname(dirname(rl_path)) - -@everywhere import Pkg -@everywhere Pkg.activate(dl_path) -@everywhere Pkg.instantiate() -@everywhere using DecisionRules - -@everywhere Pkg.activate(rl_path) -@everywhere Pkg.instantiate() - -@everywhere import QuickPOMDPs: QuickPOMDP -@everywhere import POMDPTools: ImplicitDistribution, Deterministic, FunctionPolicy -@everywhere import Distributions: Normal, Uniform -@everywhere using POMDPs -@everywhere using Flux -@everywhere using Crux -@everywhere import Crux: state_space -@everywhere using Random -@everywhere using Distributions -@everywhere using CommonRLInterface -@everywhere using CommonRLSpaces - -@everywhere using Ipopt, HSL_jll -@everywhere HydroPowerModels_dir = joinpath(dirname(rl_path), "HydroPowerModels") -@everywhere include(joinpath(HydroPowerModels_dir, "load_hydropowermodels.jl")) - -@everywhere case_name = "bolivia" # bolivia, case3 -@everywhere formulation = "ACPPowerModel" # SOCWRConicPowerModel, DCPPowerModel, ACPPowerModel -@everywhere num_stages = 96 # 96, 48 - -import Crux: new_ep_reset! -Crux.new_ep_reset!(π::ContinuousNetwork) = begin - Flux.reset!(π.network) -end - -@everywhere function build_mdp( - case_name, - formulation, - num_stages; - solver=optimizer_with_attributes( - Ipopt.Optimizer, - "print_level" => 0, - "hsllib" => HSL_jll.libhsl_path, - "linear_solver" => "ma27", - ), - penalty=nothing, - _objective_value=objective_value, -) - formulation_file = formulation * ".mof.json" - Random.seed!(1234) - subproblems, state_params_in, state_params_out, uncertainty_samples, initial_state, max_volume = build_hydropowermodels( - joinpath(HydroPowerModels_dir, case_name), - formulation_file; - num_stages=num_stages, - penalty=penalty, - ) - - for subproblem in subproblems - set_optimizer(subproblem, solver) - end - - # test solve - JuMP.optimize!(subproblems[1]) - termination_status(subproblems[1]) - - Random.seed!(1234) - uncertainty_sample = DecisionRules.sample(uncertainty_samples)[1] - rain_state = [uncertainty_sample[i][2] for i in 1:length(uncertainty_sample)] - - num_a = length(state_params_in[1]) - mdp = QuickPOMDP( - actions=Box(zeros(num_a), max_volume), - obstype=Array{Float64,1}, - discount=0.995, - gen=function (state, state_out, rng) - state_in, rain_state, j = state[1:num_a], - state[(num_a + 1):(end - 1)], - ceil(Int, state[end]) - rain = if j == num_stages - DecisionRules.sample(uncertainty_samples[j]) - else - DecisionRules.sample(uncertainty_samples[j + 1]) - end - rain = [rain[i][2] for i in 1:length(rain)] - # Scale tanh output [-1,1] to physical action range matching TS-GDR scale - state_out = - ((state_out .+ 1) ./ 2) .* (max_volume .+ rain_state .* 0.0036) ./ 10 - uncertainty_sample = [ - (uncertainty_samples[j][i][1], rain_state[i]) for i in 1:length(rain) - ] - simulate_stage( - subproblems[j], - state_params_in[j], - state_params_out[j], - uncertainty_sample, - state_in, - state_out, - ) - r = _objective_value(subproblems[j]) - sp = DecisionRules.get_next_state( - subproblems[j], state_params_in[j], state_params_out[j], state_in, state_out - ) - @info "Stage t=$j" sum(state_in) sum(rain_state) sum(state_out) sum(sp) r - sp = [sp; rain; j+1] - o = sp # no hidden state - return (sp=sp, o=o, r=(-r)) - end, - initialstate=Deterministic([initial_state; rain_state; 1.0]), - initialobs=(s) -> Deterministic(s), - isterminal=s -> s[end] > num_stages, - ) - return mdp, num_a, max_volume -end - -# build the MDPs -@everywhere mdp, num_a, max_volume = build_mdp(case_name, formulation, num_stages) -@everywhere S = state_space(mdp) - -# Define the networks we will use -@everywhere QSA() = - ContinuousNetwork(Chain(Dense(34, 64, relu), Dense(64, 64, relu), Dense(64, 1))) -@everywhere V() = - ContinuousNetwork(Chain(Dense(2*num_a+1, 64, relu), Dense(64, 64, relu), Dense(64, 1))) -@everywhere A() = ContinuousNetwork( - Chain(Dense(2*num_a+1, 64, relu), Dense(64, 64, relu), Dense(64, num_a, tanh)) -) -@everywhere SG() = SquashedGaussianPolicy( - ContinuousNetwork( - Chain(Dense(2*num_a+1, 64, relu), Dense(64, 64, relu), Dense(64, num_a)) - ), - zeros(Float32, 1), - 1.0f0, -) - -# Solve with REINFORCE -@everywhere 𝒮_reinforce = REINFORCE(π=SG(), S=S, N=10000, ΔN=10, a_opt=(batch_size=10,)) -@time π_reinforce = solve(𝒮_reinforce, mdp) - -# Solve with PPO -@everywhere 𝒮_ppo = PPO( - π=ActorCritic(SG(), V()), S=S, N=10000, ΔN=10, a_opt=(batch_size=10,) -) -@time π_ppo = solve(𝒮_ppo, mdp) - -# Off-policy settings -@everywhere off_policy = ( - S=S, - ΔN=10, - N=10000, - buffer_size=Int(200), - buffer_init=200, - c_opt=(batch_size=128, optimizer=Adam(1e-3)), - a_opt=(batch_size=128, optimizer=Adam(1e-3)), - π_explore=GaussianNoiseExplorationPolicy(0.5f0, a_min=[0.0], a_max=[1.0]), -) - -# Solver with DDPG -@everywhere 𝒮_ddpg = DDPG(; π=ActorCritic(A(), QSA()), off_policy...) -@time π_ddpg = solve(𝒮_ddpg, mdp) - -# Solve with TD3 -@everywhere 𝒮_td3 = TD3(; π=ActorCritic(A(), DoubleNetwork(QSA(), QSA())), off_policy...) -@time π_td3 = solve(𝒮_td3, mdp) - -# Solve with SAC -@everywhere 𝒮_sac = SAC(; π=ActorCritic(SG(), DoubleNetwork(QSA(), QSA())), off_policy...) -@time π_sac = solve(𝒮_sac, mdp) - -####################### Plotting ####################### - -using Plots -function plot_learning_mod( - input; - title="", - ylabel="Undiscounted Return (Costs)", - xlabel="Training Steps", - values=:undiscounted_return, - labels=:default, - legend=:bottomright, - font=:palatino, - p=plot(), - vertical_lines=[], - vline_range=(0, 1), - thick_every=1, - yscale=:log10, - xs=nothing, -) - dirs = directories(input) - - N = length(dirs) - values isa Symbol && (values = fill(values, N)) - if labels == :default - labels = N == 1 ? [""] : ["Task $i" for i in 1:N] - end - - # Plot the vertical lines (usually for multitask learning or to designate a point on the curve) - for i in 1:length(vertical_lines) - plot!( - p, - [vertical_lines[i], vertical_lines[i]], - [vline_range...]; - color=:black, - linewidth=i % thick_every == 0 ? 3 : 1, - label="", - ) - end - - # Plot the learning curves - plot!( - p; - ylabel=ylabel, - xlabel=xlabel, - legend=legend, - title=title, - fontfamily=font, - yscale=yscale, - ) - for i in 1:length(dirs) - x, y = readtb(dirs[i], values[i]) - if xs != nothing - x = x[xs] - y = y[xs] - end - plot!(p, x, -y; alpha=0.3, color=i, label="", yscale=yscale) - plot!(p, x, smooth(-y); color=i, label=labels[i], linewidth=2, yscale=yscale) - end - return p -end - -p = plot_learning_mod( - [𝒮_reinforce, 𝒮_ppo, 𝒮_ddpg, 𝒮_td3, 𝒮_sac]; - title="LTHD Training Curves", - labels=["REINFORCE", "PPO", "DDPG", "TD3", "SAC"], - legend=:outertopright, - yscale=:log10, -) - -using CSV, DataFrames -tsgdr_df = CSV.read("../../tsgdr_train.csv", DataFrame) - -plot!( - p, - tsgdr_df[:, "Step"], - tsgdr_df[ - :, "bolivia-ACPPowerModel-h96-2024-05-18T17:06:09.485 - metrics/training_loss" - ]; - label="TS-GDR", - linewidth=2, - yscale=:log10, -) - -Crux.savefig("./hydro_benchmark.pdf") diff --git a/examples/RL/test.jl b/examples/RL/test.jl deleted file mode 100644 index 26c57be..0000000 --- a/examples/RL/test.jl +++ /dev/null @@ -1,62 +0,0 @@ -import QuickPOMDPs: QuickPOMDP -import POMDPTools: ImplicitDistribution -import Distributions: Normal, Uniform -using POMDPs -using Flux -using Crux -import Crux: state_space -using POMDPs -import POMDPTools: FunctionPolicy -using Random -using Distributions - -mdp = QuickPOMDP(; - actions=[-1.0, 0.0, 1.0], - obstype=Array{Float64,1}, - discount=0.95, - transition=function (s, a) - ImplicitDistribution() do rng - x, v = s - vp = v + a*0.001 + cos(3*x)*-0.0025 + 0.0002*randn(rng) - vp = clamp(vp, -0.07, 0.07) - xp = x + vp - return (xp, vp) - end - end, - observation=(a, sp) -> MvNormal([sp[1]], [0.15][:, :]), - reward=function (s, a, sp) - if sp[1] > 0.5 - return 100.0 - else - return -1.0 - end - end, - initialstate=ImplicitDistribution(rng -> (-0.2*rand(rng), 0.0)), - isterminal=s -> s[1] > 0.5, - initialobs=(s) -> MvNormal([s[1]], [0.15][:, :]), -) - -function Crux.state_space(mdp::QuickPOMDP; μ=0.0f0, σ=1.0f0) - s = rand(initialstate(mdp)) - o = rand(initialobs(mdp, s)) - - return state_space(o; μ=μ, σ=σ) -end - -as = [actions(mdp)...] -amin = minimum(as) -amax = maximum(as) - -rand_policy = FunctionPolicy((s) -> Float32.(rand.(Uniform.(amin, amax)))) - -S = state_space(mdp) - -function SG() - return SquashedGaussianPolicy( - ContinuousNetwork(Chain(Dense(1, 64, relu), Dense(64, 64, relu), Dense(64, 1))), - zeros(Float32, 1), - 2.0f0, - ) -end -𝒮_reinforce = REINFORCE(; π=SG(), S=S, N=100000, ΔN=2048, a_opt=(batch_size=512,)) -@time π_reinforce = solve(𝒮_reinforce, mdp) diff --git a/examples/inventory_control/README.md b/examples/inventory_control/README.md index a4ae4be..0b97023 100644 --- a/examples/inventory_control/README.md +++ b/examples/inventory_control/README.md @@ -68,6 +68,52 @@ subproblems. Two strategies are available: For the relaxed formulation (no integer variables), `NoIntegerStrategy` is used — subproblems are solved and duals read as-is. +## Strict Mode (reachable policy, no penalty tuning) + +The `strict` and `strict_integer` variants replace the L1 target penalty with +the hard equality `s_mid == s_target` — no deficit variable, no penalty +hyperparameter, and the target-constraint dual is the pure shadow price +(the same construction as the hydro strict mode). + +Strict mode requires every target to be one-stage feasible. Here the exact +reachable set is a one-liner: with realized incoming inventory `s` and order +`q ∈ [0, Q_max]`, the order-up-to position satisfies + +``` +s_mid = s + q ∈ [s, s + Q_max] +``` + +and nothing else constrains it (`s_out` is free; the hold/backlog split is +always feasible). `InventoryReachablePolicy` maps the network output onto +exactly this interval. Three design points, each load-bearing: + +1. **Boundary attainment.** With fixed ordering cost `K`, "do not order" + (`q = 0`) is an essential decision at the interval boundary. The policy + uses `hardsigmoid` (attains 0 and 1 exactly on finite inputs) instead of + `sigmoid` (strictly interior) — a strict-sigmoid policy would be forced + to pay `K` every period. In the hydro case the boundary is economically + irrelevant and this distinction does not matter; here it is first-order. +2. **Pass-through state components.** The demand-history entries of the + state have singleton reachable sets (the observed demands): the policy + passes them through deterministically, and the stage models leave their + target parameters unconstrained. +3. **Stage-wise training only.** Strict variants train on the stage-wise + subproblems (closed loop). The target (`s_mid`, pre-demand) and the + carried state (`s_out = s_mid − d`, post-demand) are different + quantities, so the deterministic equivalent's target-feedback recursion + would hand the policy the wrong state for its reachable bounds — unlike + hydro, where target and state are the same reservoir volume and the + strict regular DE is safe by induction. + +Run them like any other variant (one tag per invocation): + +```bash +julia --project=examples/inventory_control \ + examples/inventory_control/train_dr_inventory.jl strict +julia --project=examples/inventory_control \ + examples/inventory_control/train_dr_inventory.jl strict_integer +``` + ## Score-Function Gradient Mixing `ScoreFunctionConfig` adds a REINFORCE-style correction to the dual diff --git a/examples/inventory_control/build_inventory_problem.jl b/examples/inventory_control/build_inventory_problem.jl index b6137b8..a18c579 100644 --- a/examples/inventory_control/build_inventory_problem.jl +++ b/examples/inventory_control/build_inventory_problem.jl @@ -152,9 +152,16 @@ Returns the five-tuple expected by `simulate_multistage` and - `T`, `K`, `c`, `h`, `p`, `Q_max`: problem parameters. - `I_0`: initial inventory. - `num_scenarios`: number of uncertainty samples per SGD batch. -- `penalty`: target-deficit penalty λ. +- `penalty`: target-deficit penalty λ (ignored when `strict = true`). - `seed`: RNG seed for demand sampling. - `integer`: whether to include binary setup variable z. +- `strict`: replace the L1 target penalty with the hard equality + `s_mid == s_target` (no deficit variable, no penalty hyperparameter). + Requires a policy whose targets are always one-stage reachable, i.e. + `s_target ∈ [s_in, s_in + Q_max]` — see [`InventoryReachablePolicy`](@ref). + The demand pass-through targets stay unconstrained in both modes: their + reachable sets are singletons (the observed demands), so constraining them + adds nothing and predicting them is not a decision. """ function build_inventory_subproblems(; T = INVENTORY_T, @@ -168,6 +175,7 @@ function build_inventory_subproblems(; penalty = INVENTORY_PENALTY, seed = 42, integer = true, + strict = false, ) # Fix the random seed so demand samples are reproducible. Random.seed!(seed) @@ -242,9 +250,19 @@ function build_inventory_subproblems(; # Split end-of-period inventory into holding and backlog parts. @constraint(m, inv_hold - back == s_out) - # L1 target-deficit penalty: λ · |s_mid - ŝ_target|. - _, deficit = create_deficit!(m, 1; penalty_l1=penalty) - @constraint(m, deficit[1] == s_mid - s_target) + if strict + # Strict mode: hard equality, no slack, no penalty term. The dual + # of this constraint is the pure shadow price ∂q_t/∂ŝ_target. + # Feasible iff s_target ∈ [s_in, s_in + Q_max] (exactly the + # one-stage reachable set of s_mid = s_in + q with q ∈ [0, Q_max]; + # s_out and the hold/backlog split are free, so no other + # constraint restricts s_mid). + @constraint(m, s_mid == s_target) + else + # L1 target-deficit penalty: λ · |s_mid - ŝ_target|. + _, deficit = create_deficit!(m, 1; penalty_l1=penalty) + @constraint(m, deficit[1] == s_mid - s_target) + end # Store the model and parameter mappings. subproblems[t] = m @@ -287,9 +305,21 @@ The penalty term is - `T`, `K`, `c`, `h`, `p`, `Q_max`: problem parameters. - `I_0`: initial inventory. - `num_scenarios`: number of uncertainty samples per SGD batch. -- `penalty`: target-deficit penalty ``\\lambda``. +- `penalty`: target-deficit penalty ``\\lambda`` (ignored when `strict = true`). - `seed`: RNG seed for demand sampling. - `integer`: whether to include binary setup variable z. +- `strict`: replace the L1 penalty with hard equalities + ``s^{mid}_t = \\hat{s}_t`` and drop the penalty term from the objective. + **Feasibility caveat**: unlike the hydro case, the target (order-up-to + position ``s^{mid}``) and the carried state (post-demand inventory + ``s^{out} = s^{mid} - d``) are *different quantities*, so the standard + target-feedback DE recursion hands the policy ``\\hat{s}^{mid}_{t-1}`` + where a reachable policy expects ``s^{out}_{t-1}`` — the induction + guarantee does NOT transfer naively. Either train stage-wise (what the + `strict` variants do; see `build_training_problem`) or reconstruct the + realized inventory inside the recursion as + ``s^{out}_{t-1} = \\hat{s}_{t-1} - d_{t-1}`` (target minus the demand + pass-through) before computing reachable bounds. # Examples ```julia @@ -311,6 +341,7 @@ function build_inventory_det_equivalent(; penalty = INVENTORY_PENALTY, seed = 42, integer = true, + strict = false, ) # Fix the random seed so demand samples are reproducible. Random.seed!(seed) @@ -366,22 +397,40 @@ function build_inventory_det_equivalent(; # Split end-of-period inventory into holding and backlog. @constraint(m, [t=1:T], inv_hold[t] - back[t] == s_out[t]) - # --- Target-deficit penalty via NormOneCone --- - # norm_deficit_arr[t] ≥ |s_mid[t] - s_target[t]| (L1 norm). - @variable(m, norm_deficit_arr[1:T] >= 0.0) - @variable(m, deficit_arr[1:T]) - @constraint(m, [t=1:T], deficit_arr[t] == s_mid[t] - s_target[t]) - @constraint(m, [t=1:T], [norm_deficit_arr[t]; deficit_arr[t:t]] in MOI.NormOneCone(2)) + if strict + # --- Strict targets: hard equalities, no deficit machinery --- + # Feasible for any target path with ŝ_t ∈ [s_{t-1}, s_{t-1} + Q_max] + # generated by a reachable policy: by induction, the equality pins + # s_mid[t] (hence s_out[t] = ŝ_t − d_t) to the value the policy + # planned from, so every later target stays reachable. + @constraint(m, [t=1:T], s_mid[t] == s_target[t]) - # --- Objective: operational cost + target penalty --- - if integer - @objective(m, Min, - sum(K * z[t] + c * q[t] + h * inv_hold[t] + p * back[t] for t in 1:T) + - penalty * sum(norm_deficit_arr)) + # --- Objective: pure operational cost --- + if integer + @objective(m, Min, + sum(K * z[t] + c * q[t] + h * inv_hold[t] + p * back[t] for t in 1:T)) + else + @objective(m, Min, + sum(c * q[t] + h * inv_hold[t] + p * back[t] for t in 1:T)) + end else - @objective(m, Min, - sum(c * q[t] + h * inv_hold[t] + p * back[t] for t in 1:T) + - penalty * sum(norm_deficit_arr)) + # --- Target-deficit penalty via NormOneCone --- + # norm_deficit_arr[t] ≥ |s_mid[t] - s_target[t]| (L1 norm). + @variable(m, norm_deficit_arr[1:T] >= 0.0) + @variable(m, deficit_arr[1:T]) + @constraint(m, [t=1:T], deficit_arr[t] == s_mid[t] - s_target[t]) + @constraint(m, [t=1:T], [norm_deficit_arr[t]; deficit_arr[t:t]] in MOI.NormOneCone(2)) + + # --- Objective: operational cost + target penalty --- + if integer + @objective(m, Min, + sum(K * z[t] + c * q[t] + h * inv_hold[t] + p * back[t] for t in 1:T) + + penalty * sum(norm_deficit_arr)) + else + @objective(m, Min, + sum(c * q[t] + h * inv_hold[t] + p * back[t] for t in 1:T) + + penalty * sum(norm_deficit_arr)) + end end # --- Build parameter mappings for DecisionRules interface --- @@ -623,3 +672,172 @@ function build_lstm_exante_policy(; seed=2024, hidden=16) return LSTMExAntePolicy(encoder, combiner, state) end + +# --------------------------------------------------------------------------- +# Reachable ex-ante policy (strict mode: always-feasible targets) +# --------------------------------------------------------------------------- + +@doc raw""" + InventoryReachablePolicy{E,C,S} + +Recurrent ex-ante policy whose targets are always one-stage reachable, +enabling **strict mode** (hard target equalities, no penalty hyperparameter). + +## Exact reachable set + +The only decision behind the target is the order quantity +``q \in [0, Q_{max}]`` with ``s^{mid} = s + q``, where ``s`` is the realized +incoming inventory. ``s^{mid}`` appears in no other restrictive constraint +(``s^{out}`` is free and the hold/backlog split is feasible for any sign), so +the one-stage reachable set of the target is **exactly** + +```math +\hat{s} \in [\, s,\; s + Q_{max} \,], +``` + +with no conservatism and no cross-component coupling. The binary setup +variable does not shrink this set (``z = 1`` admits every +``q \in [0, Q_{max}]``); it only reshapes the cost over it. + +## Boundary attainment (why `hardsigmoid`, not `sigmoid`) + +With a fixed ordering cost ``K > 0``, the endpoint ``q = 0`` ("do not +order") is an economically essential decision, not a measure-zero boundary. +A strict sigmoid keeps ``q = Q_{max}\,\sigma(z) > 0`` for every finite +``z``, which under strict equalities forces ``z_{setup} = 1`` and pays +``K`` every period — silently deleting the no-order action from the policy +class. The output therefore uses `hardsigmoid`, which attains 0 and 1 +exactly on finite inputs: + +```math +\hat{s} = s + Q_{max} \cdot \operatorname{hard\sigma}(z), \qquad +\operatorname{hard\sigma}(z) = \min(\max(z/6 + 1/2,\, 0),\, 1). +``` + +(Contrast with the hydro reachable policy, where the interval endpoints are +economically irrelevant and a strict sigmoid is harmless.) + +## Pass-through components + +The second and third state components (``d_{t-1}``, ``d_{t-2}`` histories) +are exogenous information states: their one-stage "reachable sets" are +singletons — the realized demand values. The policy passes them through +deterministically (no trainable parameters); the stage models leave their +target parameters unconstrained in both penalty and strict modes. + +## Information pattern and gradients + +Strictly **ex-ante**: the LSTM encoder consumes only the *lagged* demand +``d_{t-1}``; current demand ``d_t`` is never used for the order decision +(it appears in the output only as state pass-through plumbing). The bound +map ``\hat{s} = s + Q_{max} y`` is affine and exact, so — unlike the hydro +policy's physics bounds — it is left differentiable: the additive ``s`` +term carries an exact gradient path through the realized state. + +# Fields +- `encoder::E`: `Flux.LSTMCell` over the normalized lagged demand. +- `combiner::C`: `Dense` head mapping `[encoded; s/100; d_{t-2}/100]` to the + pre-activation order fraction. +- `state::S`: LSTM hidden state, threaded across stages; reset per scenario. +- `Q_max::Float32`: order capacity (the constant reachable-interval width). + +# Examples +```julia +policy = build_reachable_inventory_policy(; seed = 2024) +Flux.reset!(policy) +target = policy(Float32[d_t, inventory, d_lag1, d_lag2]) +@assert inventory <= target[1] <= inventory + INVENTORY_Q_MAX +``` +""" +mutable struct InventoryReachablePolicy{E,C,S} + encoder::E + combiner::C + state::S + Q_max::Float32 +end + +Functors.@functor InventoryReachablePolicy (encoder, combiner) + +function (policy::InventoryReachablePolicy)(x) + # Extract features from input: [d_t, inventory, d_{t-1}, d_{t-2}]. + # Only d_{t-1} (lagged) feeds the LSTM — d_t is NOT used (ex-ante). + inventory = Float32(x[2]) + last_demand = Float32(x[3]) + prev_demand = Float32(x[4]) + + # Match the element type of the LSTM state (Float32 during training). + T = eltype(first(policy.state)) + + # Feed the normalized lagged demand through the LSTM cell and thread + # the hidden state to the next stage call within this scenario. + encoded, new_state = policy.encoder(T[last_demand / 100], policy.state) + policy.state = new_state + + # Concatenate LSTM output with current inventory and prev demand. + combined = vcat(encoded, T[inventory / 100, prev_demand / 100]) + + # Map to the order fraction y ∈ [0, 1]; hardsigmoid attains both + # endpoints exactly (q = 0 and q = Q_max are real decisions). + y = Flux.hardsigmoid(policy.combiner(combined)[1]) + + # Affine map onto the exact reachable interval [s, s + Q_max], computed + # in Float64 FROM THE RAW INPUT STATE: the implied order quantity is then + # q = target − s = Q_max·y ≥ 0 bitwise. Using the Float32-cast state here + # would make the strict equality infeasible whenever y saturates at 0 and + # Float32(s) < s (the solver would need q ≈ −1e−5·s < 0) — a failure mode + # the integer variant hits constantly because the fixed cost K actively + # pushes the policy to the q = 0 boundary. + target_s_mid = Float64(x[2]) + Float64(policy.Q_max) * Float64(y) + + # Return [target_s_mid, d_t, d_{t-1}] — target + state pass-throughs. + return [target_s_mid, Float64(x[1]), Float64(last_demand)] +end + +""" + Flux.reset!(policy::InventoryReachablePolicy) -> Nothing + +Reset the LSTM hidden state to its initial value. Must be called at every +scenario boundary so demand-history memory does not leak across rollouts. +""" +function Flux.reset!(policy::InventoryReachablePolicy) + # Restore the LSTM hidden state to its fresh initial values. + policy.state = Flux.initialstates(policy.encoder) + return nothing +end + +""" + build_reachable_inventory_policy(; seed = 2024, hidden = 16, Q_max = INVENTORY_Q_MAX) + -> InventoryReachablePolicy + +Construct the reachable ex-ante policy used by the strict variants. + +Architecture matches [`build_lstm_exante_policy`](@ref) exactly — +LSTMCell(1 → hidden) encoder, Dense(hidden + 2 → 1) head — so +penalty-vs-strict comparisons isolate the output parameterization, not +model capacity. + +# Keyword Arguments +- `seed::Int`: random seed for weight initialization. +- `hidden::Int`: LSTM hidden dimension. +- `Q_max`: order capacity (reachable-interval width). + +# Examples +```julia +policy = build_reachable_inventory_policy(; seed = 2024, hidden = 16) +``` +""" +function build_reachable_inventory_policy(; seed=2024, hidden=16, Q_max=INVENTORY_Q_MAX) + # Fix the random seed for reproducible weight initialization. + Random.seed!(seed) + + # LSTM cell: 1 input (normalized lagged demand) → hidden state. + encoder = Flux.LSTMCell(1 => hidden) + + # Dense head: [LSTM output; inventory; prev_demand] → order fraction. + combiner = Dense(hidden + 2, 1) + + # Initialize the LSTM hidden state to its default zeros. + state = Flux.initialstates(encoder) + + return InventoryReachablePolicy(encoder, combiner, state, Float32(Q_max)) +end diff --git a/examples/inventory_control/compare_results.jl b/examples/inventory_control/compare_results.jl index f9d6f2e..b53671e 100644 --- a/examples/inventory_control/compare_results.jl +++ b/examples/inventory_control/compare_results.jl @@ -803,6 +803,7 @@ function relaxed_results() lstm_costs = optional_costs("relaxed_lstm", "dr") hp_costs = optional_costs("relaxed_hp", "dr") lstm_hp_costs = optional_costs("relaxed_lstm_hp", "dr") + strict_costs = optional_costs("strict", "dr") # Load scalar baseline metadata. base_stock_level = read_scalar(resolve_file("relaxed_basestock_S_star.txt")) @@ -820,6 +821,8 @@ function relaxed_results() push!(results, MethodResult("TS-DDR Relaxed (LSTM)", lstm_costs)) !isnothing(lstm_hp_costs) && push!(results, MethodResult("TS-DDR Relaxed (LSTM+HP)", lstm_hp_costs)) + !isnothing(strict_costs) && + push!(results, MethodResult("TS-DDR Strict (Reachable)", strict_costs)) # Append non-TS-DDR baselines. push!(results, MethodResult("SDDP (PAR)", sddp_costs)) @@ -834,6 +837,8 @@ function relaxed_results() push!(timing_tags, "relaxed_hp") !isnothing(resolve_file_optional("relaxed_lstm_hp_dr_timing.csv")) && push!(timing_tags, "relaxed_lstm_hp") + !isnothing(resolve_file_optional("strict_dr_timing.csv")) && + push!(timing_tags, "strict") return results, load_timing(timing_tags), base_stock_level, sddp_bound end @@ -862,6 +867,7 @@ function integer_results() hp_costs = optional_costs("integer_hp", "dr") lstm_costs = optional_costs("integer_lstm", "dr") lstm_sf_costs = optional_costs("integer_lstm_sf", "dr") + strict_integer_costs = optional_costs("strict_integer", "dr") # Load scalar baseline metadata. base_stock_level = read_scalar(resolve_file("integer_basestock_S_star.txt")) @@ -882,6 +888,8 @@ function integer_results() push!(results, MethodResult("TS-DDR (LSTM)", lstm_costs)) !isnothing(lstm_sf_costs) && push!(results, MethodResult("TS-DDR (LSTM+SF)", lstm_sf_costs)) + !isnothing(strict_integer_costs) && + push!(results, MethodResult("TS-DDR Strict (Reachable+Int)", strict_integer_costs)) # Append non-TS-DDR baselines. push!(results, MethodResult("SDDP (MIP fwd)", sddp_mip_forward_costs)) @@ -891,7 +899,7 @@ function integer_results() # Collect timing tags for all present variants. timing_tags = ["integer", "integer_cr"] - for tag in ["integer_sf", "integer_hp", "integer_lstm", "integer_lstm_sf"] + for tag in ["integer_sf", "integer_hp", "integer_lstm", "integer_lstm_sf", "strict_integer"] !isnothing(resolve_file_optional("$(tag)_dr_timing.csv")) && push!(timing_tags, tag) end diff --git a/examples/inventory_control/train_dr_inventory.jl b/examples/inventory_control/train_dr_inventory.jl index 7777e1b..bff6228 100644 --- a/examples/inventory_control/train_dr_inventory.jl +++ b/examples/inventory_control/train_dr_inventory.jl @@ -115,6 +115,23 @@ struct InventoryTrainingVariant penalty::Float64 policy_builder::Function penalty_schedule_fn::Function + # Strict mode: hard target equalities (no deficit, no penalty). Requires a + # reachable policy — see InventoryReachablePolicy and strict_variants(). + strict::Bool +end + +# Backward-compatible 11-argument constructor: every historical call site +# predates strict mode, so it defaults to the penalty formulation. +function InventoryTrainingVariant( + tag, integer, num_batches, train_per_batch, learning_rate, warmup_batches, + training_integer_strategy, score_function, penalty, policy_builder, + penalty_schedule_fn, +) + return InventoryTrainingVariant( + tag, integer, num_batches, train_per_batch, learning_rate, warmup_batches, + training_integer_strategy, score_function, penalty, policy_builder, + penalty_schedule_fn, false, + ) end function InventoryTrainingVariant( @@ -168,6 +185,20 @@ function penalty_schedule_for(variant::InventoryTrainingVariant) return [warmup_phase, full_penalty_phase] end +""" + no_penalty_schedule(variant::InventoryTrainingVariant) -> Nothing + +Return `nothing`: strict-mode models have no deficit variables, so there is +no penalty to schedule (`train_multistage` accepts +`penalty_schedule = nothing`). + +# Examples +```julia +schedule = no_penalty_schedule(variant) # nothing +``` +""" +no_penalty_schedule(::InventoryTrainingVariant) = nothing + """ method_label(variant::InventoryTrainingVariant) -> String @@ -184,6 +215,10 @@ label = method_label(variant) function method_label(variant::InventoryTrainingVariant) tag = variant.tag + # --- Strict variants (reachable policy, no penalty) --- + tag == "strict" && return "TS-DDR Strict (Reachable)" + tag == "strict_integer" && return "TS-DDR Strict (Reachable+Int)" + # --- Relaxed tuned variants --- tag == "relaxed_lstm" && return "TS-DDR Relaxed (LSTM)" tag == "relaxed_hp" && return "TS-DDR Relaxed (HighPenalty)" @@ -403,7 +438,27 @@ det_eq, state_in, state_out, sampler, initial_state = ``` """ function build_training_problem(variant::InventoryTrainingVariant) - # Training uses a deterministic equivalent so target-dual gradients are coupled. + if variant.strict + # Strict variants train STAGE-WISE (closed loop). In this problem the + # target (order-up-to position s_mid) and the carried state + # (post-demand inventory s_out = s_mid − d) are different quantities, + # so the deterministic equivalent's target-feedback recursion would + # hand the reachable policy s_mid where it expects s_out — computing + # the reachable interval from the wrong state and breaking the strict + # feasibility guarantee. Stage-wise training feeds realized states, + # for which the interval [s, s + Q_max] is exact. (Contrast with the + # hydro case, where target and carried state are the same reservoir + # volume and the strict regular DE is safe by induction.) + return build_inventory_subproblems(; + num_scenarios = N_TRAIN_SCENARIOS, + seed = 42, + integer = variant.integer, + strict = true, + ) + end + + # Penalty variants use a deterministic equivalent so target-dual gradients + # are coupled across stages. return build_inventory_det_equivalent(; num_scenarios = N_TRAIN_SCENARIOS, penalty = variant.penalty, @@ -433,6 +488,7 @@ function build_evaluation_problem(variant::InventoryTrainingVariant) penalty = variant.penalty, seed = 99, integer = variant.integer, + strict = variant.strict, ) end @@ -485,6 +541,37 @@ function estimate_initial_loss( ) end +# Stage-wise method (strict variants train on subproblems with realized-state +# feedback): rolls the policy in closed loop, matching the training semantics. +function estimate_initial_loss( + policy, + subproblems::Vector{JuMP.Model}, + state_params_in, + state_params_out, + uncertainty_sampler, + initial_state, + variant::InventoryTrainingVariant, +) + # Use a small fixed sample only to seed SaveBest with a finite baseline. + Random.seed!(111) + + return mean( + let uncertainty_sample = sample(uncertainty_sampler) + # Closed-loop rollout: each stage sees the realized state. + Flux.reset!(policy) + simulate_multistage( + subproblems, + state_params_in, + state_params_out, + initial_state, + uncertainty_sample, + policy; + integer_strategy = variant.training_integer_strategy, + ) + end for _ in 1:12 + ) +end + """ train_variant!(policy, variant, det_eq, state_params_in, state_params_out, uncertainty_sampler, initial_state, model_path, curve_path; @@ -528,7 +615,12 @@ train_variant!(policy, variant, det_eq, spi, spo, sampler, x0, function train_variant!( policy, variant::InventoryTrainingVariant, - det_eq::JuMP.Model, + # Penalty variants train on the deterministic equivalent (JuMP.Model); + # strict variants train stage-wise (Vector{JuMP.Model}, realized-state + # feedback) because the target (order-up-to position s_mid) and the + # carried state (post-demand inventory s_out) are different quantities — + # target feedback would hand the reachable policy the wrong state. + det_eq::Union{JuMP.Model,Vector{JuMP.Model}}, state_params_in, state_params_out, uncertainty_sampler, @@ -577,6 +669,12 @@ function train_variant!( # Fix optimizer randomness for repeatability. Random.seed!(2024) + # The score-function keyword exists only on the deterministic-equivalent + # overload of train_multistage; the stage-wise overload (strict variants) + # must not receive it. + score_function_kwargs = det_eq isa JuMP.Model ? + (score_function = variant.score_function,) : NamedTuple() + elapsed_seconds = @elapsed train_multistage( policy, initial_state, @@ -589,7 +687,7 @@ function train_variant!( optimizer = Flux.Adam(variant.learning_rate), integer_strategy = variant.training_integer_strategy, penalty_schedule = variant.penalty_schedule_fn(variant), - score_function = variant.score_function, + score_function_kwargs..., record = (sample_log, iteration, current_policy) -> begin loss = isempty(sample_log.objectives_no_deficit) ? NaN : @@ -1084,6 +1182,45 @@ function inventory_training_variants() ), # Variant B: LSTM with tuned score function lstm_score_function_variant(), + # --- Strict variants (hard target equalities, no penalty tuning) --- + # The reachable policy guarantees ŝ ∈ [s, s + Q_max] exactly, so the + # strict equality s_mid == ŝ is always feasible and its dual is the + # pure shadow price — no penalty hyperparameter, no annealing. + InventoryTrainingVariant( + "strict", + false, + 800, + 10, + 1.0e-3, + 120, + NoIntegerStrategy(), + nothing, + INVENTORY_PENALTY, # unused in strict mode + () -> build_reachable_inventory_policy(; seed = 2024), + no_penalty_schedule, + true, + ), + # Strict + binary setup: FixedDiscreteIntegerStrategy reads exact LP + # shadow prices at the fixed integer assignment. The K·z jump at + # ŝ = s (order/no-order switch) is invisible to that local dual; a + # mixed score-function gradient can be layered on later using + # PENALTY-mode rollout models (ScoreFunctionConfig owns its own + # subproblems), since strict rollouts would reject perturbed targets + # that leave the reachable interval. + InventoryTrainingVariant( + "strict_integer", + true, + 800, + 10, + 8.0e-4, + 120, + FixedDiscreteIntegerStrategy(), + nothing, + INVENTORY_PENALTY, # unused in strict mode + () -> build_reachable_inventory_policy(; seed = 2024), + no_penalty_schedule, + true, + ), ] end diff --git a/examples/solve_dataset.jl b/examples/solve_dataset.jl deleted file mode 100644 index dcb3554..0000000 --- a/examples/solve_dataset.jl +++ /dev/null @@ -1,96 +0,0 @@ -################################################################ -###################### Dataset Generation ###################### -################################################################ - -using Distributed -using Random - -############## -# Load Functions -############## - -@everywhere l2o_path = dirname(@__DIR__) - -@everywhere import Pkg - -@everywhere Pkg.activate(l2o_path) - -@everywhere Pkg.instantiate() - -########## SCRIPT REQUIRED PACKAGES ########## - -@everywhere using L2O -@everywhere using UUIDs -@everywhere import ParametricOptInterface as POI -@everywhere using JuMP -@everywhere using UUIDs -@everywhere using Arrow - -## SOLVER PACKAGES ## - -@everywhere using Ipopt - -@everywhere filetype = ArrowFile - -########## PARAMETERS ########## -model_file = joinpath( - l2o_path, "examples/HydroPowerModels/case3/ACPPowerModel_det_equivalent.mof.json" -) -input_file = joinpath( - l2o_path, - "examples/HydroPowerModels/case3/case3_ACPPowerModel_input_4c4e8974-040e-11ef-1398-8195139913f4", -) -state_name = ["reservoir"; "out"] -save_path = joinpath(l2o_path, "examples/HydroPowerModels/case3/ACPPowerModel/output") -case_name = String(split(split(model_file, ".mof.")[1], "/")[end]) -processed_output_files = [ - file for file in readdir(save_path; join=true) if occursin(case_name, file) -] -ids = if length(processed_output_files) == 0 - UUID[] -else - vcat([Vector(Arrow.Table(file).id) for file in processed_output_files]...) -end -batch_size = 200 - -########## SOLVE ########## - -problem_iterator_factory, num_batches = load( - model_file, input_file, filetype; batch_size=batch_size, ignore_ids=ids -) - -@sync @distributed for i in 1:num_batches - ipopt = Ipopt.Optimizer() - MOI.set(ipopt, MOI.RawOptimizerAttribute("print_level"), 0) - cached = - () -> MOI.Bridges.full_bridge_optimizer( - MOI.Utilities.CachingOptimizer( - MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()), ipopt - ), - Float64, - ) - POI_cached_optimizer() = POI.Optimizer(cached()) - batch_id = uuid1() - problem_iterator = problem_iterator_factory(i) - set_optimizer(problem_iterator.model, () -> POI_cached_optimizer()) - output_file = joinpath(save_path, "$(case_name)_output_$(batch_id)") - all_vars = all_variables(problem_iterator.model) - states = all_vars[findall( - x -> all([occursin(part, name(x)) for part in state_name]), all_vars - )] - recorder = Recorder{filetype}( - output_file; - primal_variables=states, - filterfn=(model) -> true, - model=problem_iterator.model, - ) - successfull_solves = solve_batch(problem_iterator, recorder) - @info "Solved $(length(successfull_solves)) problems" - L2O.compress_batch_arrow( - save_path, - case_name; - keyword_all="output", - batch_id=string(batch_id), - keyword_any=[string(batch_id)], - ) -end diff --git a/src/DecisionRules.jl b/src/DecisionRules.jl index 5f90a6a..882e934 100644 --- a/src/DecisionRules.jl +++ b/src/DecisionRules.jl @@ -17,6 +17,7 @@ export simulate_multistage, simulate_states, simulate_stage, dense_multilayer_nn, + dense_policy_head, variable_to_parameter, create_deficit!, default_annealed_schedule, @@ -32,6 +33,10 @@ export simulate_multistage, ContinuousRelaxationIntegerStrategy, StallingCriterium, policy_input_dim, + ContextualPolicy, + context_at, + stage_phase_context, + vcat_contexts, normalize_recur_state, StateConditionedPolicy, state_conditioned_policy, @@ -113,11 +118,53 @@ tests to ensure that controlled problems never silently produce zero gradients. """ struct ErrorGradientFallback <: AbstractGradientFallback end +""" + _zero_cotangents(n_in, n_out) + +Create a tuple of zero/no tangents compatible with the `get_next_state` rrule pullback signature. + +Used by [`handle_gradient_error`](@ref) to produce a safe, neutral gradient when the +solver or DiffOpt differentiation fails. The returned tuple matches the cotangent +layout expected by `ChainRulesCore.rrule` for `get_next_state`: +four `NoTangent()` entries (for the function itself and non-differentiable arguments), +followed by dense zero vectors for the state-in and state-out dimensions, and a +trailing `NoTangent()`. + +# Arguments +- `n_in::Int`: dimension of the incoming state vector. +- `n_out::Int`: dimension of the outgoing state vector. + +# Returns +A `Tuple` of `NoTangent` and `Vector{Float64}` elements that Zygote can propagate +without error. +""" _zero_cotangents(n_in, n_out) = ( NoTangent(), NoTangent(), NoTangent(), NoTangent(), zeros(n_in), zeros(n_out), NoTangent(), ) +""" + handle_gradient_error(fallback::AbstractGradientFallback, e, n_state_in, n_state_out) + +Handle an exception raised inside the `get_next_state` rrule pullback. + +This is the **rrule-level** extension point: it is called when the backward pass +through a single stage fails (e.g., the solver returned an infeasible status and +DiffOpt cannot differentiate). Concrete methods decide whether to absorb the +error or propagate it. + +# Arguments +- `fallback::AbstractGradientFallback`: dispatch tag controlling recovery behavior. +- `e`: the caught exception. +- `n_state_in::Int`: dimension of the incoming state vector (needed to build zero cotangents). +- `n_state_out::Int`: dimension of the outgoing state vector. + +# Returns +A cotangent tuple (same layout as [`_zero_cotangents`](@ref)) when the error is +absorbed, or does not return (re-throws) when the error is propagated. + +See [`AbstractGradientFallback`](@ref) for how to implement custom subtypes. +""" function handle_gradient_error(::ZeroGradientFallback, e, n_state_in, n_state_out) @warn "get_next_state pullback failed — returning zero gradients" exception=(e, catch_backtrace()) return _zero_cotangents(n_state_in, n_state_out) @@ -127,6 +174,27 @@ function handle_gradient_error(::ErrorGradientFallback, e, n_state_in, n_state_o rethrow(e) end +""" + handle_training_error(fallback::AbstractGradientFallback, e, iter) + +Handle an exception raised during a full training iteration (gradient computation +and parameter update). + +This is the **training-loop-level** extension point: it is called when +`Zygote.gradient` or the subsequent optimizer update throws (e.g., a DiffOpt +assertion error or a numerical issue in the loss computation). Unlike +[`handle_gradient_error`](@ref), which operates inside a single-stage rrule, +this handler wraps the entire forward-backward pass for one iteration. + +# Arguments +- `fallback::AbstractGradientFallback`: dispatch tag controlling recovery behavior. +- `e`: the caught exception. +- `iter::Int`: current training iteration index (used in log messages). + +# Returns +- `true` to skip this iteration and continue training. +- Does not return (re-throws) when the error should propagate. +""" function handle_training_error(::ZeroGradientFallback, e, iter) @warn "Gradient computation failed at iter $iter — skipping update" exception=(e, catch_backtrace()) return true @@ -136,6 +204,25 @@ function handle_training_error(::ErrorGradientFallback, e, iter) rethrow(e) end +""" + handle_rollout_error(fallback::AbstractGradientFallback, e, iter) + +Handle an exception raised during a rollout evaluation scenario. + +This is the **rollout-level** extension point: it is called when a single +out-of-sample scenario fails during [`RolloutEvaluation`](@ref) (e.g., solver +infeasibility on an unseen uncertainty sample). Absorbing the error skips that +scenario and lets the evaluation continue with the remaining samples. + +# Arguments +- `fallback::AbstractGradientFallback`: dispatch tag controlling recovery behavior. +- `e`: the caught exception. +- `iter::Int`: scenario index within the rollout batch. + +# Returns +- `true` to skip this scenario and continue the rollout. +- Does not return (re-throws) when the error should propagate. +""" function handle_rollout_error(::ZeroGradientFallback, e, iter) @warn "Rollout scenario failed at iter $iter — skipping" exception=(e, catch_backtrace()) return true diff --git a/src/dense_multilayer_nn.jl b/src/dense_multilayer_nn.jl index 113ffac..f19578c 100644 --- a/src/dense_multilayer_nn.jl +++ b/src/dense_multilayer_nn.jl @@ -1,17 +1,97 @@ using Functors using ChainRulesCore +raw""" + dense_policy_head(num_inputs, num_outputs, layers; activation=Flux.relu) + +Create the feed-forward target head used after recurrent uncertainty encoding. + +Unlike [`dense_multilayer_nn`](@ref), the final layer also uses `activation`. +This is useful for bounded policies where the head must emit normalized values +such as `sigmoid(z) ∈ [0, 1]`. + +Mathematically, when `layers = [h₁, …, h_L]`, the head computes + +```math +H_\theta(z) = +\sigma\left(W_{L+1} + \sigma\left(W_L \cdots \sigma(W_1 z + b_1) \cdots + b_L\right) + + b_{L+1}\right). +``` + +This helper exists so state-conditioned TS-DDR policies can make the map +`[encoded_uncertainty; state] → target` nonlinear while keeping recurrence +confined to the uncertainty encoder. + +# Arguments +- `num_inputs::Int`: number of combined features, usually `encoded_uncertainty` + concatenated with the previous state. +- `num_outputs::Int`: number of target outputs. +- `layers::AbstractVector{Int}`: hidden widths for the nonrecurrent head. +- `activation`: activation used by each hidden layer and by the output layer. + +# Returns +- A `Dense` layer when `layers` is empty, otherwise a `Chain` of dense layers. + +# Examples +```julia +head = dense_policy_head(16, 3, [32, 32]; activation=sigmoid) +``` + +See also: [`state_conditioned_policy`](@ref), [`dense_multilayer_nn`](@ref) +""" +function dense_policy_head( + num_inputs::Int, + num_outputs::Int, + layers::AbstractVector{Int}; + activation=Flux.relu, +) + isempty(layers) && return Dense(num_inputs => num_outputs, activation) + head_layers = Any[Dense(num_inputs => layers[1], activation)] + for i in 1:(length(layers) - 1) + push!(head_layers, Dense(layers[i] => layers[i + 1], activation)) + end + push!(head_layers, Dense(layers[end] => num_outputs, activation)) + return Chain(head_layers...) +end + """ dense_multilayer_nn(num_inputs, num_outputs, layers; activation=Flux.relu, dense=Dense) Create a multi-layer neural network with the specified architecture. +Given hidden layer widths ``(h_1, \\dots, h_L)`` and activation ``\\sigma``, +the resulting `Chain` computes + +```math +f(x) = W_{L+1} \\, \\sigma\\bigl(W_L \\cdots \\sigma(W_1 x + b_1) \\cdots + b_L\\bigr) + b_{L+1}, +``` + +where ``W_k \\in \\mathbb{R}^{h_k \\times h_{k-1}}``, ``h_0 = \\text{num\\_inputs}``, +and the final layer ``W_{L+1} \\in \\mathbb{R}^{\\text{num\\_outputs} \\times h_L}`` +has no activation. When `layers` is empty, there is no hidden/output +distinction: the returned single dense layer uses `activation`, preserving the +same constructor semantics as `Dense(num_inputs, num_outputs, activation)`. + +If `dense` is a recurrent type (`LSTM`, `GRU`, `RNN`), pair notation +`in => out` is used instead of `(in, out, σ)`, and the last layer omits the +activation so it can act as a linear projection. + # Arguments -- `num_inputs::Int`: Number of input features -- `num_outputs::Int`: Number of output features -- `layers::Vector{Int}`: Hidden layer sizes -- `activation`: Activation function (default: Flux.relu) -- `dense`: Layer type (Dense, LSTM, etc.) +- `num_inputs::Int`: number of input features ``h_0``. +- `num_outputs::Int`: number of output features. +- `layers::Vector{Int}`: hidden layer widths ``(h_1, \\dots, h_L)``. +- `activation`: element-wise activation ``\\sigma`` (default: `Flux.relu`). +- `dense`: layer constructor (`Dense`, `LSTM`, `GRU`, `RNN`). + +# Returns +- `Chain` (or a single layer when `layers` is empty). + +# Examples +```julia +mlp = dense_multilayer_nn(10, 3, [64, 32]) # 10 → 64 → 32 → 3 +rnn = dense_multilayer_nn(5, 2, [16]; dense=LSTM) # 5 → 16 → 2 (LSTM) +``` """ function dense_multilayer_nn( num_inputs::Int, @@ -20,9 +100,14 @@ function dense_multilayer_nn( activation=Flux.relu, dense=Dense, ) + # Recurrent layers use pair notation (in => out) and ignore activation. is_recurrent = dense in (LSTM, GRU, RNN) + + # Helper that builds one hidden layer with the appropriate constructor form. _make_layer(in_dim, out_dim) = is_recurrent ? dense(in_dim => out_dim) : dense(in_dim, out_dim, activation) + + # No hidden layers: preserve Dense(input, output, activation) semantics. if length(layers) == 0 return if is_recurrent dense(num_inputs => num_outputs) @@ -30,10 +115,17 @@ function dense_multilayer_nn( dense(num_inputs, num_outputs, activation) end end + + # Interior hidden layers: h_2 → h_3 → … → h_{L-1}. midlayers = [_make_layer(layers[i], layers[i + 1]) for i in 1:(length(layers) - 1)] + + # First hidden layer maps from the input dimension. first_layer = _make_layer(num_inputs, layers[1]) + + # Final layer omits activation so the output is an unrestricted linear map. last_layer = is_recurrent ? dense(layers[end] => num_outputs) : dense(layers[end], num_outputs) + return Chain(first_layer, midlayers..., last_layer) end @@ -42,62 +134,200 @@ end Compute the input dimension for a policy network. -Policy networks receive `[uncertainty..., previous_state...]` as input, -so the input dimension is `num_uncertainties + num_states`. +Policy networks receive ``[w_t;\\; x_{t-1}]`` as input, so the required +dimension is -This format is consistent between subproblems and deterministic equivalent -formulations, enabling warmstarting policies trained with det_eq for use +```math +d_{\\text{in}} = \\dim(w_t) + \\dim(x_{t-1}). +``` + +This format is consistent between subproblem and deterministic-equivalent +formulations, enabling warm-starting policies trained with det_eq for use with subproblems. # Arguments -- `num_uncertainties::Int`: Number of uncertainty parameters per stage -- `num_states::Int`: Number of state variables +- `num_uncertainties::Int`: dimensionality of the stage uncertainty ``w_t``. +- `num_states::Int`: dimensionality of the state ``x_{t-1}``. # Returns -- `Int`: Total input dimension for the policy network +- `Int`: total input dimension ``d_{\\text{in}}``. + +# Examples +```julia +d = policy_input_dim(5, 3) # 8 +``` """ function policy_input_dim(num_uncertainties::Int, num_states::Int) + # Input is the concatenation [w_t; x_{t-1}]. return num_uncertainties + num_states end +function policy_input_dim(num_uncertainties::Int, num_states::Int, num_context::Int) + # Input is the concatenation [context_t; w_t; x_{t-1}]. + return num_context + num_uncertainties + num_states +end + """ policy_input_dim(uncertainty_samples, initial_state) Compute the input dimension for a policy network from problem data. +Infers ``\\dim(w_t)`` from the first uncertainty sample and ``\\dim(x)`` +from the initial state vector, then delegates to +[`policy_input_dim(::Int, ::Int)`](@ref). + # Arguments -- `uncertainty_samples`: Uncertainty samples from problem construction -- `initial_state`: Initial state vector +- `uncertainty_samples::Vector`: uncertainty samples; `length(uncertainty_samples[1])` + gives ``\\dim(w_t)``. +- `initial_state::Vector`: initial state vector; `length(initial_state)` gives + ``\\dim(x)``. # Returns -- `Int`: Total input dimension for the policy network +- `Int`: total input dimension ``d_{\\text{in}}``. + +# Examples +```julia +d = policy_input_dim(uncertainty_samples, x0) +``` """ function policy_input_dim(uncertainty_samples::Vector, initial_state::Vector) + # Infer dimensions from the first sample and the initial state. num_uncertainties = length(uncertainty_samples[1]) num_states = length(initial_state) return policy_input_dim(num_uncertainties, num_states) end +""" + ContextualPolicy(policy, context) + +Wrap a stage policy so each call receives known exogenous context before the +usual policy input. The wrapped policy is called as +`policy(vcat(context_at(context, t), input))`, where `t` is advanced once per +call and reset by `Flux.reset!`. + +This keeps training and rollout loops unchanged: context is data attached to +the trajectory, while the inner policy remains the only trainable component. +Matrix contexts are interpreted as `d_context x T`; function contexts are +called as `context(t)`. +""" +mutable struct ContextualPolicy{P,C} + policy::P + context::C + t::Int +end + +ContextualPolicy(policy, context) = ContextualPolicy(policy, context, 0) + +Functors.@functor ContextualPolicy (policy,) + +""" + context_at(context, t) + +Return the context vector for one-based stage `t`. +""" +function context_at(context::AbstractMatrix, t::Integer) + 1 <= t <= size(context, 2) || + throw(BoundsError(context, (:, t))) + return view(context, :, t) +end + +context_at(context::Function, t::Integer) = context(t) + +function (m::ContextualPolicy)(input) + m.t += 1 + return m.policy(vcat(context_at(m.context, m.t), input)) +end + +function Flux.reset!(m::ContextualPolicy) + m.t = 0 + Flux.reset!(m.policy) + return nothing +end + +""" + stage_phase_context(T; period, include_progress=true) + +Build a `d x T` context matrix encoding known calendar/stage information. +Rows are `sin(2*pi*t/period)`, `cos(2*pi*t/period)`, and, when +`include_progress=true`, `t/T`. + +The sine/cosine pair represents a cyclic process without a discontinuity +between the final and first period positions. A raw index would put those +neighbors far apart; one-hot period features would be exact but high +dimensional and would not encode adjacency. The optional progress feature has +a different purpose: it tells the policy how close it is to the optimization +horizon endpoint. +""" +function stage_phase_context(T::Integer; period::Integer, include_progress::Bool=true) + T >= 1 || throw(ArgumentError("T must be positive")) + period >= 1 || throw(ArgumentError("period must be positive")) + nrows = include_progress ? 3 : 2 + ctx = Matrix{Float32}(undef, nrows, T) + for t in 1:T + θ = 2f0 * Float32(pi) * Float32(t) / Float32(period) + ctx[1, t] = sin(θ) + ctx[2, t] = cos(θ) + if include_progress + ctx[3, t] = Float32(t) / Float32(T) + end + end + return ctx +end + +""" + vcat_contexts(a, b, ...) + +Vertically concatenate context matrices after checking that they cover the +same number of stages. +""" +function vcat_contexts(contexts::AbstractMatrix...) + isempty(contexts) && return Matrix{Float32}(undef, 0, 0) + T = size(first(contexts), 2) + all(size(c, 2) == T for c in contexts) || + throw(ArgumentError("all contexts must have the same number of columns")) + return vcat(contexts...) +end + """ StateConditionedPolicy -A policy architecture that separates temporal encoding from state conditioning: -- `encoder`: a recurrent cell (`LSTMCell`/`GRUCell`/`RNNCell`, or a `Chain` of them) - that encodes only the uncertainty sequence (temporal dependencies) -- `combiner`: a `Dense` layer that combines the encoder output with the previous - state to produce the next state +A policy architecture that separates temporal encoding from state conditioning. -Flux's recurrent cells are stateless (Flux >= 0.16): each call returns -`(output, new_state)` instead of mutating an internal `Recur`. `StateConditionedPolicy` -therefore carries the encoder's recurrent state itself in `state`, threading it through -one call per stage. Call `Flux.reset!` to clear it (back to `Flux.initialstates`) at the -start of a rollout. +The encoder is a recurrent cell (`LSTMCell`/`GRUCell`/`RNNCell`, or a `Chain` +of cells) that processes only the uncertainty sequence to capture temporal +dependencies. The combiner is a feed-forward target head that merges the +encoder output with the previous state to predict the next state. + +Given uncertainty ``w_t`` and previous state ``x_{t-1}``, the forward pass is + +```math +h_t, s_t = \\text{LSTM}(w_t, s_{t-1}) \\\\ +\\hat{x}_t = f_{\\text{combine}}([h_t;\\; x_{t-1}]) +``` -Input format: [uncertainty..., previous_state...] +where ``s_t`` is the hidden recurrent state carried across stages and +``f_{\\text{combine}}`` is a `Dense` layer with activation ``\\sigma``. + +Flux's recurrent cells are stateless (Flux >= 0.16): each call returns +`(output, new_state)` instead of mutating an internal `Recur`. +`StateConditionedPolicy` therefore carries the encoder's recurrent state +itself in `state`, threading it through one call per stage. Call +`Flux.reset!` to clear it (back to `Flux.initialstates`) at the start of +a rollout. + +# Fields +- `encoder::E`: recurrent cell or `Chain` of cells encoding uncertainty. +- `combiner::C`: feed-forward head mapping ``[h_t;\\; x_{t-1}]`` to + ``\\hat{x}_t``. +- `state::S`: current recurrent state ``s_t``, carried across calls. +- `n_uncertainty::Int`: dimensionality of ``w_t``. +- `n_state::Int`: dimensionality of ``x_{t-1}``. + +Input format: `[uncertainty..., previous_state...]`. """ mutable struct StateConditionedPolicy{E,C,S} encoder::E # Recurrent cell, or Chain of cells, that processes uncertainty only - combiner::C # Dense that combines encoder output with previous state + combiner::C # Feed-forward head combining encoder output with state state::S # Encoder recurrent state, carried across calls n_uncertainty::Int # Number of uncertainty dimensions n_state::Int # Number of state dimensions @@ -108,41 +338,91 @@ end Functors.@functor StateConditionedPolicy (encoder, combiner) """ - materialize_tangent(x) - -Recursively convert ChainRulesCore tangent types (MutableTangent, Tangent) -to plain NamedTuples/Arrays that Flux.update! can handle. + materialize_tangent(x::Number) -> Number -This is needed because Zygote produces MutableTangent for mutable structs (like Flux.Recur), -but Flux.update!/Optimisers.jl expects plain NamedTuples. +Return numeric tangents unchanged (leaf values are already plain scalars). """ materialize_tangent(x::Number) = x + +""" + materialize_tangent(x::AbstractArray) -> AbstractArray + +Return array tangents unchanged (arrays are already `Flux.update!`-compatible). +""" materialize_tangent(x::AbstractArray) = x + +""" + materialize_tangent(::Nothing) -> Nothing + +Map `nothing` tangents (unused parameters) to `nothing`. +""" materialize_tangent(::Nothing) = nothing + +""" + materialize_tangent(::ChainRulesCore.NoTangent) -> Nothing + +Map `NoTangent` (structural zeros for non-differentiable fields) to `nothing`. +""" materialize_tangent(::ChainRulesCore.NoTangent) = nothing + +""" + materialize_tangent(::ChainRulesCore.ZeroTangent) -> Nothing + +Map `ZeroTangent` (additive identity in the tangent space) to `nothing`. +""" materialize_tangent(::ChainRulesCore.ZeroTangent) = nothing +""" + materialize_tangent(t::ChainRulesCore.MutableTangent) -> NamedTuple + +Unwrap a `MutableTangent` (produced by Zygote for mutable structs such as +`Flux.Recur`) by extracting its backing `NamedTuple` and recursing. +""" function materialize_tangent(t::ChainRulesCore.MutableTangent) - # MutableTangent stores values in RefValues, extract them + # MutableTangent stores values in RefValues; extract them via backing. backing = ChainRulesCore.backing(t) return materialize_tangent(backing) end +""" + materialize_tangent(t::ChainRulesCore.Tangent) -> NamedTuple + +Unwrap an immutable `Tangent` by extracting its backing and recursing. +""" function materialize_tangent(t::ChainRulesCore.Tangent) + # Tangent backing is already a NamedTuple; recurse to handle nested types. backing = ChainRulesCore.backing(t) return materialize_tangent(backing) end +""" + materialize_tangent(nt::NamedTuple{K}) -> NamedTuple{K} + +Recurse through every field of a `NamedTuple`, materializing each value. +""" function materialize_tangent(nt::NamedTuple{K}) where {K} + # Apply materialize_tangent element-wise and reconstruct the same keys. vals = map(materialize_tangent, values(nt)) return NamedTuple{K}(vals) end +""" + materialize_tangent(t::Tuple) -> Tuple + +Recurse through every element of a `Tuple`, materializing each value. +""" function materialize_tangent(t::Tuple) return map(materialize_tangent, t) end +""" + materialize_tangent(ref::Base.RefValue) + +Dereference a `RefValue` wrapper (used inside `MutableTangent` fields) +and recurse on the contained value. +""" function materialize_tangent(ref::Base.RefValue) + # RefValue wraps a single value; extract it before recursing. return materialize_tangent(ref[]) end @@ -153,6 +433,7 @@ Return the underlying recurrent cell of `layer`. `Flux.LSTM`/`GRU`/`RNN` wrap a (`LSTMCell`/`GRUCell`/`RNNCell`) in a `.cell` field; if `layer` has no such field it is already a cell and is returned unchanged. """ +# Unwrap .cell field if present (LSTM → LSTMCell); return unchanged otherwise. _as_cell(layer) = hasfield(typeof(layer), :cell) ? layer.cell : layer """ @@ -161,7 +442,9 @@ _as_cell(layer) = hasfield(typeof(layer), :cell) ? layer.cell : layer Return the initial recurrent state for `encoder`: `Flux.initialstates(encoder)` for a single cell, or a tuple of per-layer initial states for a `Chain` of cells. """ +# Single cell: return (h0, c0) or equivalent from Flux.initialstates. _init_recurrent_state(cell) = Flux.initialstates(cell) +# Chain of cells: one initial state per layer, returned as a tuple. _init_recurrent_state(chain::Chain) = map(_init_recurrent_state, chain.layers) """ @@ -171,38 +454,106 @@ Advance `encoder` by one step on input `x` from recurrent `state`, returning the output and the updated state. For a `Chain` of cells, each layer's output feeds the next and each layer's state is threaded independently. """ +# Single cell: one stateful call returns (output, new_state). _step_encoder(cell, x, state) = cell(x, state) function _step_encoder(chain::Chain, x, states::Tuple) + # Delegate to the recursive tuple-based implementation. return _step_encoder_layers(chain.layers, x, states) end +""" + _step_encoder_layers(layers, x, states) -> (output, new_states) + +Recursively advance a tuple of recurrent layers by one time step. + +Each layer receives the output of the previous layer as input and its own +independent recurrent state. The base case (`layers == ()`) returns the +input unchanged with an empty state tuple. + +# Arguments +- `layers::Tuple`: remaining recurrent cells to evaluate. +- `x`: current input (or output of the prior layer). +- `states::Tuple`: per-layer recurrent states, same length as `layers`. + +# Returns +- `output`: output of the last layer in `layers`. +- `new_states::Tuple`: updated recurrent states, one per layer. +""" _step_encoder_layers(::Tuple{}, x, ::Tuple{}) = x, () function _step_encoder_layers(layers::Tuple, x, states::Tuple) + # Advance the first layer with its own recurrent state. out, new_state = _step_encoder(first(layers), x, first(states)) + + # Recurse on remaining layers, feeding this layer's output as input. rest_out, rest_states = _step_encoder_layers(Base.tail(layers), out, Base.tail(states)) + + # Reassemble the full state tuple: this layer's state followed by the rest. return rest_out, (new_state, rest_states...) end +""" + _state_eltype(state) -> Type + +Return the scalar element type of a recurrent state. + +For nested tuple states (e.g. LSTM's `(h, c)` or a `Chain`'s tuple of +per-layer states) this recurses into the first element until it reaches an +`AbstractVector`, then returns `eltype(v)`. The result is used to cast +inputs to the encoder's precision before each step. + +# Arguments +- `state::Tuple`: nested recurrent state. +- `v::AbstractVector`: leaf state vector. + +# Returns +- `Type`: the scalar element type (e.g. `Float32`). +""" _state_eltype(state::Tuple) = _state_eltype(first(state)) _state_eltype(v::AbstractVector) = eltype(v) +""" + (m::StateConditionedPolicy)(x) -> AbstractVector + +Execute the forward pass of the state-conditioned policy. + +The input `x` is split into the uncertainty portion ``w_t`` and the previous +state ``x_{t-1}``. The forward pass computes + +```math +h_t, s_t = \\text{encoder}(w_t, s_{t-1}) \\\\ +\\hat{x}_t = f_{\\text{combine}}([h_t;\\; x_{t-1}]) +``` + +where ``s_t`` is the updated recurrent state (stored in `m.state` for the +next call) and ``f_{\\text{combine}}`` is a `Dense` layer. + +# Arguments +- `x::AbstractVector`: concatenated input `[w_t..., x_{t-1}...]` of length + `m.n_uncertainty + m.n_state`. + +# Returns +- `AbstractVector`: predicted next state ``\\hat{x}_t``. +""" function (m::StateConditionedPolicy)(x) - # Split input into uncertainty and previous state + # Split the concatenated input into uncertainty w_t and previous state x_{t-1}. uncertainty = x[1:m.n_uncertainty] prev_state = x[(m.n_uncertainty + 1):end] - # Encode uncertainty through the recurrent encoder, carrying state across calls. - # Cast to encoder precision to keep the recurrent state type stable - # (avoids a Zygote codegen bug with nested-tuple convert when solver - # feeds Float64 into a Float32 LSTM). + # Determine the encoder's scalar precision from its current recurrent state. + # Casting the input avoids a Zygote codegen bug with nested-tuple convert + # when an upstream solver feeds Float64 into a Float32 LSTM. T = _state_eltype(m.state) + + # Advance the recurrent encoder by one step: h_t, s_t = encoder(w_t, s_{t-1}). encoded, new_state = _step_encoder(m.encoder, T.(uncertainty), m.state) + + # Persist the new recurrent state so the next call starts from s_t. m.state = new_state - # Combine encoded uncertainty with previous state + # Concatenate encoder output h_t with previous state x_{t-1}. combined = vcat(encoded, prev_state) - # Output next state prediction + # Map the combined vector through the feed-forward combiner to produce x_hat_t. return m.combiner(combined) end @@ -213,29 +564,62 @@ Reset the encoder's recurrent state to `Flux.initialstates`, e.g. before startin new rollout. """ function Flux.reset!(m::StateConditionedPolicy) + # Reinitialize s_0 to the cell's default (zeros for LSTM h/c). m.state = _init_recurrent_state(m.encoder) return nothing end """ state_conditioned_policy(n_uncertainty, n_state, n_output, layers; - activation=Flux.relu, encoder_type=Flux.LSTM) + activation=Flux.relu, encoder_type=Flux.LSTM, + combiner_layers=Int[]) + +Create a [`StateConditionedPolicy`](@ref) with the specified architecture. + +The resulting policy computes + +```math +h_t, s_t = \\text{encoder}(w_t, s_{t-1}) \\\\ +\\hat{x}_t = \\sigma\\bigl(W [h_t;\\; x_{t-1}] + b\\bigr) +``` -Create a StateConditionedPolicy with the specified architecture. +where the encoder is a stack of recurrent cells of width +``(l_1, \\dots, l_L)`` and the combiner has input dimension +``l_L + n_{\\text{state}}``. `combiner_layers` adds optional hidden layers +to this nonrecurrent combiner, making the state-to-target map nonlinear while +keeping recurrence confined to the uncertainty sequence. This distinction is +important for TS-DDR hydro experiments: inflow history is recurrent, but the +current reservoir state is used only as contemporaneous conditioning. # Arguments -- `n_uncertainty::Int`: Number of uncertainty input dimensions -- `n_state::Int`: Number of state dimensions (both input and output) -- `n_output::Int`: Number of output dimensions (typically same as n_state) -- `layers::Vector{Int}`: Hidden layer sizes for the encoder -- `activation`: Activation function for dense layers (default: relu) -- `encoder_type`: Recurrent layer/cell type (`LSTM`, `GRU`, `RNN`, or their `*Cell` - variants; default: `Flux.LSTM`). Must support `Flux.initialstates` and the stateful - `(x, state) -> (output, new_state)` call (Flux >= 0.16). - -# Architecture -- Encoder: encoder_type(n_uncertainty => layers[1]) -> ... -> layers[end] -- Combiner: Dense(layers[end] + n_state => n_output) +- `n_uncertainty::Int`: dimensionality of the uncertainty input ``w_t``. +- `n_state::Int`: dimensionality of the state ``x_{t-1}`` (both input and output). +- `n_output::Int`: output dimension (typically equal to `n_state`). +- `layers::Vector{Int}`: hidden layer widths ``(l_1, \\dots, l_L)`` for the encoder. +- `activation`: activation ``\\sigma`` for the combiner `Dense` layer + (default: `Flux.relu`). +- `encoder_type`: recurrent layer/cell constructor (`LSTM`, `GRU`, `RNN`, or + their `*Cell` variants; default: `Flux.LSTM`). Must support + `Flux.initialstates` and the stateful `(x, state) -> (output, new_state)` + interface (Flux >= 0.16). +- `combiner_layers::Vector{Int}`: hidden widths for the state-conditioned + target head. The default `Int[]` preserves the original single Dense head. + +# Returns +- `StateConditionedPolicy`: ready-to-use policy with initialized recurrent state. + +# Examples +```julia +policy = state_conditioned_policy(5, 3, 3, [16, 16]) +x = randn(Float32, 8) # [uncertainty(5); state(3)] +y = policy(x) # predicted next state (length 3) + +deep_head = state_conditioned_policy( + 5, 3, 3, [16, 16]; + activation = sigmoid, + combiner_layers = [32, 32], +) +``` """ function state_conditioned_policy( n_uncertainty::Int, @@ -244,28 +628,38 @@ function state_conditioned_policy( layers::Vector{Int}; activation=Flux.relu, encoder_type=Flux.LSTM, + combiner_layers=Int[], ) - # Build encoder (stack of recurrent cells that process uncertainty) + # Build encoder: a stack of recurrent cells that process only the uncertainty + # input w_t. The number of hidden layers determines the encoder topology. if length(layers) == 0 + # No hidden layers: single cell maps uncertainty directly to n_state. encoder = _as_cell(encoder_type(n_uncertainty => n_state)) encoder_output_dim = n_state elseif length(layers) == 1 + # One hidden layer: single cell with the specified width. encoder = _as_cell(encoder_type(n_uncertainty => layers[1])) encoder_output_dim = layers[1] else + # Multiple hidden layers: chain of cells, first maps from n_uncertainty. encoder_layers = [_as_cell(encoder_type(n_uncertainty => layers[1]))] for i in 1:(length(layers) - 1) + # Each subsequent cell maps from the previous layer's width. push!(encoder_layers, _as_cell(encoder_type(layers[i] => layers[i + 1]))) end encoder = Chain(encoder_layers...) encoder_output_dim = layers[end] end - # Build combiner (Dense that combines encoder output with previous state) - # Input: [encoded_uncertainty, previous_state] - # Output: next_state - combiner = Dense(encoder_output_dim + n_state => n_output, activation) + # Build combiner: feed-forward [h_t; x_{t-1}] → x_hat_t with activation σ. + combiner = dense_policy_head( + encoder_output_dim + n_state, + n_output, + collect(Int, combiner_layers); + activation=activation, + ) + # Initialize the recurrent state to Flux.initialstates for the chosen cell(s). return StateConditionedPolicy( encoder, combiner, _init_recurrent_state(encoder), n_uncertainty, n_state ) diff --git a/src/multiple_shooting.jl b/src/multiple_shooting.jl index 322d174..5173c54 100644 --- a/src/multiple_shooting.jl +++ b/src/multiple_shooting.jl @@ -33,6 +33,16 @@ Assumptions: using Base: accumulate using Zygote: Zygote +""" + _print_window_status_and_params(window, status; context="") + +Print a diagnostic dump for a window solve that did not reach an optimal status. + +Outputs: a primal feasibility report for `window.model`, the solver `status`, +the `window.stage_range`, and a sorted listing of every JuMP parameter in the +window model with its current value. The optional `context` string is included +in the header for traceability. +""" function _print_window_status_and_params(window, status; context::AbstractString="") header = isempty(context) ? "solve_window status" : "solve_window status ($context)" println("=====") @@ -92,6 +102,13 @@ function extract_uncertainty_params(window_uncertainties) end end +""" + _param_init_value(src::JuMP.VariableRef) -> Float64 + +Return the current parameter value of `src` if it is a JuMP `MOI.Parameter`; +otherwise return `0.0`. Silently returns `0.0` if `parameter_value` throws +(e.g., when the parameter has been deleted from the model). +""" function _param_init_value(src::JuMP.VariableRef) if JuMP.is_parameter(src) try @@ -103,6 +120,13 @@ function _param_init_value(src::JuMP.VariableRef) return 0.0 end +""" + _as_float64_vec(val) -> Vector{Float64} + +Coerce `val` to a `Vector{Float64}`. If `val` is already a `Vector{Float64}`, +return it as-is; if it is another `AbstractVector`, convert element-wise; if it +is a scalar, wrap it in a single-element vector. +""" function _as_float64_vec(val) if val isa AbstractVector return val isa Vector{Float64} ? val : Float64.(val) @@ -110,6 +134,16 @@ function _as_float64_vec(val) return [Float64(val)] end +""" + _create_like_variable(m::JuMP.Model, src::JuMP.VariableRef, t::Int; + force_parameter=false) -> JuMP.VariableRef + +Create a new variable in `m` that mirrors `src`. If `src` is a JuMP parameter +(or `force_parameter` is `true`), the new variable is created as an +`MOI.Parameter` initialized to [`_param_init_value`](@ref)`(src)`; otherwise a +free decision variable is created. The variable is named via +[`var_set_name!`](@ref) with stage suffix `t`. +""" function _create_like_variable( m::JuMP.Model, src::JuMP.VariableRef, t::Int; force_parameter::Bool=false ) @@ -368,6 +402,15 @@ function solve_window( ) end +""" + _set_window_parameters!(window_state_in_params, window_state_out_params, + s_in, targets) -> Nothing + +Write numeric initial-state and target values into the JuMP `MOI.Parameter` +variables of a window model before solving. `s_in` is broadcast into +`window_state_in_params`; each element of `targets` is broadcast into the +corresponding stage's target parameters in `window_state_out_params`. +""" function _set_window_parameters!( window_state_in_params, window_state_out_params, diff --git a/src/parameter_duals.jl b/src/parameter_duals.jl index 581f8d5..21b6c58 100644 --- a/src/parameter_duals.jl +++ b/src/parameter_duals.jl @@ -33,8 +33,19 @@ model = Model(HiGHS.Optimizer) @constraint(model, con, x >= 2 * p) @objective(model, Min, 3 * x + p) optimize!(model) -dual_p = compute_parameter_dual(model, p) # Should be -2 * dual(con) + 1 +dual_p = compute_parameter_dual(model, p) # = 2 * dual(con) + 1 (constraint x - 2p >= 0 has coef -2 on p, contribution -coef*dual) +# Hand-check: at the optimum x* = 2p the objective is 7p, so d(obj)/dp = 7 = 2*3 + 1 with dual(con) = 3. ``` + +# Limitations +Only affine, quadratic, and vector-affine constraint functions are inspected: if the +parameter appears in any other constraint function type (e.g. a nonlinear +`ScalarNonlinearFunction` constraint), that contribution is skipped with a one-time +warning and is NOT captured in the returned sensitivity. + +The constraint-dual formula is validated for `MIN_SENSE` objectives; for `MAX_SENSE` +models JuMP's dual sign conventions differ and this function's constraint contribution +has not been validated. """ function compute_parameter_dual(model::JuMP.Model, param::JuMP.VariableRef) if !JuMP.is_parameter(param) @@ -73,6 +84,20 @@ function _get_dual_from_constraints(model::JuMP.Model, param::JuMP.VariableRef) dual_contribution += _get_dual_from_vector_affine_constraints( model, param, F, S ) + # Variable-in-set constraints (variable bounds, MOI.Parameter definitions) + # carry no parameter coefficient to extract: the parameter's own definition + # constraint contributes nothing here, and a parameter cannot appear inside + # another variable's bound constraint. Skip them silently. + elseif F <: JuMP.VariableRef + # Intentionally no contribution. + else + # Any other constraint function type (e.g. ScalarNonlinearFunction) is not + # inspected, so if the parameter appears there its sensitivity contribution + # is silently zero. Warn once so the gradient gap is visible, but do not + # error: constraints of these types often do not involve parameters at all. + @warn "compute_parameter_dual: constraints of type ($F, $S) are not " * + "inspected; parameter sensitivities from such constraints are not " * + "captured." maxlog = 1 end end @@ -94,15 +119,11 @@ function _get_dual_from_affine_constraints(model::JuMP.Model, param::JuMP.Variab # Check if parameter appears in this constraint coef = _get_parameter_coefficient(func, param) if !iszero(coef) - try - con_dual = JuMP.dual(con) - # The dual contribution is -coefficient * constraint_dual - # This follows from the Lagrangian: L = f(x) + λ*(g(x) - p*coef - b) - # ∂L/∂p = -λ * coef - dual_contribution -= coef * con_dual - catch - # If dual is not available, skip this constraint - end + con_dual = JuMP.dual(con) + # The dual contribution is -coefficient * constraint_dual. + # This follows from the Lagrangian: L = f(x) + λ*(g(x) - p*coef - b) + # ∂L/∂p = -λ * coef. + dual_contribution -= coef * con_dual end end @@ -132,12 +153,8 @@ function _get_dual_from_quadratic_constraints(model::JuMP.Model, param::JuMP.Var total_coef = coef + quad_coef if !iszero(total_coef) - try - con_dual = JuMP.dual(con) - dual_contribution -= total_coef * con_dual - catch - # If dual is not available, skip this constraint - end + con_dual = JuMP.dual(con) + dual_contribution -= total_coef * con_dual end end @@ -158,17 +175,12 @@ function _get_dual_from_vector_affine_constraints( con_obj = JuMP.constraint_object(con) func = con_obj.func # Vector of AffExpr - try + coefs = [_get_parameter_coefficient(expr, param) for expr in func] + if any(!iszero, coefs) con_dual = JuMP.dual(con) # Vector of duals - - for (i, expr) in enumerate(func) - coef = _get_parameter_coefficient(expr, param) - if !iszero(coef) - dual_contribution -= coef * con_dual[i] - end + for (coef, dual_entry) in zip(coefs, con_dual) + dual_contribution -= coef * dual_entry end - catch - # If dual is not available, skip this constraint end end diff --git a/src/score_function.jl b/src/score_function.jl index 120d392..b396fc0 100644 --- a/src/score_function.jl +++ b/src/score_function.jl @@ -58,6 +58,10 @@ and the mixed gradient is - `perturbation_std::Real`: Gaussian standard deviation ``\\sigma``. - `num_rollouts::Integer`: number of perturbed rollouts ``M`` per sample. - `baseline::Symbol`: either `:mean` for mean-centering costs or `:none`. + Mean-centering reduces variance but, because the baseline is computed from the + same ``M`` rollouts, it introduces a small ``O(1/M)`` bias (effectively scaling + the estimator by ``(M-1)/M``) that vanishes as `num_rollouts` grows; a + leave-one-out baseline would be exactly unbiased. # Examples ```julia @@ -509,7 +513,10 @@ function _center_rollout_costs( costs::AbstractVector{<:Real}, baseline::Symbol, ) - # A mean baseline reduces variance without changing the expected gradient. + # A mean baseline computed from the SAME rollouts reduces variance but adds a + # small O(1/M) bias (the estimator is effectively scaled by (M-1)/M); the bias + # vanishes as the rollout count grows. A leave-one-out baseline would be + # exactly unbiased; mean-centering is kept for its simplicity. baseline_value = baseline === :mean ? mean(costs) : 0.0 return Float64.(costs) .- baseline_value diff --git a/src/simulate_multistage.jl b/src/simulate_multistage.jl index a5116e7..2703b07 100644 --- a/src/simulate_multistage.jl +++ b/src/simulate_multistage.jl @@ -72,6 +72,31 @@ function simulate_stage( ) end +""" + _set_stage_parameters!(state_param_in, state_param_out, uncertainty, + state_in, state_out_target) -> Nothing + +Write MOI parameter values into a single-stage JuMP subproblem before solving. + +Three groups of parameters are set: + +1. **Incoming state** ``x_{t-1}``: each element of `state_param_in` receives + the corresponding entry of `state_in`. +2. **Uncertainty** ``w_t``: each `(parameter, value)` pair in `uncertainty` + is written directly. +3. **Outgoing target** ``\\hat{x}_t``: the first element of each tuple in + `state_param_out` (the target parameter) receives the corresponding + entry of `state_out_target`. + +After this call the subproblem is ready for `optimize!`. + +# Arguments +- `state_param_in`: JuMP parameter variables for the incoming state. +- `state_param_out`: `(target_parameter, realized_state_variable)` pairs. +- `uncertainty`: `(parameter, value)` pairs for stage uncertainty ``w_t``. +- `state_in::AbstractVector{<:Real}`: realized incoming state ``x_{t-1}``. +- `state_out_target::AbstractVector{<:Real}`: policy target ``\\hat{x}_t``. +""" function _set_stage_parameters!( state_param_in, state_param_out, @@ -79,17 +104,17 @@ function _set_stage_parameters!( state_in, state_out_target, ) - # Update state parameters + # Write the realized incoming state into the input-state parameters. for (i, state_var) in enumerate(state_param_in) set_parameter_value(state_var, state_in[i]) end - # Update uncertainty + # Write sampled exogenous values into the uncertainty parameters. for (uncertainty_param, uncertainty_value) in uncertainty set_parameter_value(uncertainty_param, uncertainty_value) end - # Update state parameters out + # Write policy targets into the output-state target parameters. for i in 1:length(state_param_out) state_var = state_param_out[i][1] set_parameter_value(state_var, state_out_target[i]) @@ -97,6 +122,27 @@ function _set_stage_parameters!( return nothing end +""" + _simulate_stage(subproblem, state_param_in, state_param_out, uncertainty, + state_in, state_out_target, integer_strategy) -> Float64 + +Forward-solve one stage of the multistage problem and return the objective. + +Sets all parameters via [`_set_stage_parameters!`](@ref), then solves +`subproblem` through [`with_sensitivity_solution`](@ref) (which applies the +`integer_strategy` for models with discrete variables). Returns the scalar +optimal objective value ``q_t(x_{t-1}, w_t; \\hat{x}_t)``. + +# Arguments +- `subproblem::JuMP.Model`: stage-``t`` JuMP model. +- `state_param_in`: incoming-state parameters. +- `state_param_out`: `(target_parameter, realized_state_variable)` pairs. +- `uncertainty`: `(parameter, value)` pairs for ``w_t``. +- `state_in`: realized incoming state ``x_{t-1}``. +- `state_out_target`: policy target ``\\hat{x}_t``. +- `integer_strategy::AbstractIntegerStrategy`: controls how discrete + variables are handled during the solve. +""" function _simulate_stage( subproblem::JuMP.Model, state_param_in, @@ -106,15 +152,57 @@ function _simulate_stage( state_out_target, integer_strategy::AbstractIntegerStrategy, ) + # Write all parameter values into the model before solving. _set_stage_parameters!( state_param_in, state_param_out, uncertainty, state_in, state_out_target ) + # Solve and extract the objective value inside the sensitivity wrapper. return with_sensitivity_solution(subproblem, integer_strategy) do sensitivity_model + # Cache the deficit-free objective while the model is clean. Integer + # strategies dirty the model on cleanup (restoring integer bounds), so + # a later logger call would otherwise find a dirty model with no cache + # and throw. Mirrors the deterministic-equivalent forward pass. + subproblem.ext[:_last_obj_no_deficit] = + get_objective_no_target_deficit(sensitivity_model) return objective_value(sensitivity_model) end end +""" + _simulate_stage_with_parameter_duals(subproblem, state_param_in, state_param_out, + uncertainty, state_in, state_out_target, + integer_strategy) + -> (objective, d_state_in, d_state_out_target) + +Forward-solve one stage and extract parameter duals for the rrule pullback. + +Like [`_simulate_stage`](@ref), this sets parameters and solves the stage +problem. In addition it reads the dual sensitivities via [`pdual`](@ref): + +```math +\\mu_t = \\frac{\\partial q_t}{\\partial x_{t-1}}, \\qquad +\\lambda_t = \\frac{\\partial q_t}{\\partial \\hat{x}_t}. +``` + +These duals are the preferred (closed-form) gradient path used by the +[`simulate_stage`](@ref) rrule when parameter duals are available from the +solver. + +# Arguments +- `subproblem`: stage-``t`` JuMP model. +- `state_param_in`: incoming-state parameters (yields ``\\mu_t``). +- `state_param_out`: target parameters (yields ``\\lambda_t``). +- `uncertainty`: `(parameter, value)` pairs for ``w_t``. +- `state_in`: realized incoming state ``x_{t-1}``. +- `state_out_target`: policy target ``\\hat{x}_t``. +- `integer_strategy::AbstractIntegerStrategy`: discrete-variable strategy. + +# Returns +- `objective::Float64`: optimal stage cost ``q_t``. +- `d_state_in::Vector{Float64}`: ``\\mu_t`` (sensitivities w.r.t. incoming state). +- `d_state_out_target::Vector{Float64}`: ``\\lambda_t`` (sensitivities w.r.t. target). +""" function _simulate_stage_with_parameter_duals( subproblem, state_param_in, @@ -124,12 +212,20 @@ function _simulate_stage_with_parameter_duals( state_out_target, integer_strategy::AbstractIntegerStrategy, ) + # Write all parameter values into the model before solving. _set_stage_parameters!( state_param_in, state_param_out, uncertainty, state_in, state_out_target ) return with_sensitivity_solution(subproblem, integer_strategy) do sensitivity_model + # Read the optimal objective value. objective = objective_value(sensitivity_model) + # Cache the deficit-free objective while the model is clean (integer + # strategies dirty the model on cleanup; see _simulate_stage). + subproblem.ext[:_last_obj_no_deficit] = + get_objective_no_target_deficit(sensitivity_model) + # Extract duals w.r.t. incoming state parameters (mu_t). d_state_in = pdual.(state_param_in) + # Extract duals w.r.t. target parameters (lambda_t). d_state_out_target = pdual.([s[1] for s in state_param_out]) return objective, d_state_in, d_state_out_target end @@ -336,29 +432,132 @@ function ChainRulesCore.rrule( return y, public_pullback end +""" + get_objective_no_target_deficit(subproblem::JuMP.Model; + norm_deficit="norm_deficit") -> Float64 + +Compute the operational cost of a solved subproblem, excluding the +target-deficit penalty. + +The full objective includes a penalty ``C_\\delta \\|\\delta_t\\|`` that +penalizes deviations between realized and target states. This function +strips those terms so that logged costs reflect true operational cost: + +```math +\\text{cost}_t = q_t - \\sum_{j \\in \\mathcal{D}} c_j \\, \\delta_j, +``` + +where ``\\mathcal{D}`` is the set of variables whose names contain +`norm_deficit`. + +If the model is dirty (parameters changed since last solve), returns the +cached value from a previous successful call (stored in +`subproblem.ext[:_last_obj_no_deficit]`). If no such value exists, or if the +objective shape is unsupported, throws instead of inventing a cost. + +# Arguments +- `subproblem::JuMP.Model`: a solved JuMP model. + +# Keywords +- `norm_deficit::AbstractString`: substring matched against variable names + to identify deficit-penalty terms. +""" function get_objective_no_target_deficit( subproblem::JuMP.Model; norm_deficit::AbstractString="norm_deficit" ) + # If parameters were changed after the last solve, return the cached value. if subproblem.is_model_dirty - return get(subproblem.ext, :_last_obj_no_deficit, 0.0) + if haskey(subproblem.ext, :_last_obj_no_deficit) + return subproblem.ext[:_last_obj_no_deficit] + end + error( + "Cannot read objective without target deficit: " * + "model is dirty and no cached value exists", + ) end - try - obj = JuMP.objective_function(subproblem) - objective_val = objective_value(subproblem) - for term in obj.terms - if occursin(norm_deficit, JuMP.name(term[1])) - objective_val -= term[2] * value(term[1]) - end + + obj = JuMP.objective_function(subproblem) + objective_val = + objective_value(subproblem) - _target_deficit_penalty_value(obj, norm_deficit) + subproblem.ext[:_last_obj_no_deficit] = objective_val + return objective_val +end + +""" + _target_deficit_penalty_value(obj, norm_deficit) -> Float64 + +Return the part of a JuMP objective expression that is attributed to target +deficit variables. A variable is treated as a target-deficit variable when its +JuMP name contains `norm_deficit`. + +Proof sketch for the affine case: if the solved objective is +`q(x) + sum_i c_i d_i`, and `d_i` are exactly the matched target-deficit +variables, then the operational cost is the solved objective value minus +`sum_i c_i value(d_i)`. + +Quadratic terms involving target-deficit variables are deliberately rejected. +For such an objective, subtracting only the affine coefficient would not remove +the whole penalty and would produce a silently biased operational cost. +""" +function _target_deficit_penalty_value( + obj::JuMP.GenericAffExpr, norm_deficit::AbstractString +) + penalty = 0.0 + for (variable, coefficient) in obj.terms + if occursin(norm_deficit, JuMP.name(variable)) + penalty += coefficient * JuMP.value(variable) end - return objective_val - catch - return get(subproblem.ext, :_last_obj_no_deficit, 0.0) end + return penalty +end + +# Quadratic objectives are allowed only when target-deficit variables appear in +# the affine part; otherwise the penalty shape is ambiguous to this helper. +function _target_deficit_penalty_value( + obj::JuMP.GenericQuadExpr, norm_deficit::AbstractString +) + for (pair, _) in obj.terms + if occursin(norm_deficit, JuMP.name(pair.a)) || + occursin(norm_deficit, JuMP.name(pair.b)) + error("Quadratic target-deficit penalty terms are unsupported") + end + end + return _target_deficit_penalty_value(obj.aff, norm_deficit) +end + +# A bare deficit variable has implicit coefficient one. +function _target_deficit_penalty_value( + obj::JuMP.VariableRef, norm_deficit::AbstractString +) + return occursin(norm_deficit, JuMP.name(obj)) ? JuMP.value(obj) : 0.0 end +_target_deficit_penalty_value(::Real, ::AbstractString) = 0.0 + +function _target_deficit_penalty_value(obj, ::AbstractString) + error("Unsupported objective type for target-deficit stripping: $(typeof(obj))") +end + +""" + get_objective_no_target_deficit(subproblems::Vector{JuMP.Model}; + norm_deficit="norm_deficit") -> Float64 + +Sum the deficit-free operational costs across all stage subproblems. + +Calls the single-model [`get_objective_no_target_deficit`](@ref) on each +element and returns the total. + +# Arguments +- `subproblems::Vector{JuMP.Model}`: one solved JuMP model per stage. + +# Keywords +- `norm_deficit::AbstractString`: substring matched against variable names + to identify deficit-penalty terms. +""" function get_objective_no_target_deficit( subproblems::Vector{JuMP.Model}; norm_deficit::AbstractString="norm_deficit" ) + # Accumulate deficit-free costs across all stages. total_objective = 0.0 for subproblem in subproblems total_objective += get_objective_no_target_deficit( @@ -368,7 +567,11 @@ function get_objective_no_target_deficit( return total_objective end -# define ChainRulesCore.rrule of get_objective_no_target_deficit +# NOTE: get_objective_no_target_deficit is intentionally NON-DIFFERENTIABLE. +# This rrule returns NoTangent() for every input, i.e. a hard-zero gradient. The +# value is a logging/metric quantity only (deficit-free operational cost read from +# an already-solved model), and it MUST NOT appear in a loss whose gradient matters: +# any dependence of the loss on the policy through this function is silently dropped. function ChainRulesCore.rrule( ::typeof(get_objective_no_target_deficit), subproblem; norm_deficit="norm_deficit" ) @@ -379,10 +582,40 @@ function ChainRulesCore.rrule( return objective_val, _pullback end +""" + apply_rule(stage::Int, decision_rule, uncertainty, state_in) -> Vector + +Apply a single (shared) policy to produce the target state for stage `stage`. + +The policy receives a concatenated input vector +``[w_t^{(1)}, \\ldots, w_t^{(n_w)}, x_{t-1}^{(1)}, \\ldots, x_{t-1}^{(n_x)}]`` +and returns the next target ``\\hat{x}_t = \\pi_\\theta(w_t, x_{t-1})``. + +# Arguments +- `stage::Int`: current stage index (unused when a single rule is shared). +- `decision_rule`: callable policy ``\\pi_\\theta``. +- `uncertainty`: `(parameter, value)` pairs for ``w_t``; values are extracted. +- `state_in`: realized incoming state ``x_{t-1}``. +""" function apply_rule(::Int, decision_rule::T, uncertainty, state_in) where {T} + # Concatenate uncertainty values and incoming state into the policy input. return decision_rule(vcat([uncertainty[i][2] for i in 1:length(uncertainty)], state_in)) end +""" + apply_rule(stage::Int, decision_rules::Vector, uncertainty, state_in) -> Vector + +Apply a stage-specific policy from a vector of per-stage decision rules. + +Dispatches to `apply_rule(stage, decision_rules[stage], uncertainty, state_in)`, +selecting the rule at index `stage`. + +# Arguments +- `stage::Int`: current stage index, used to select `decision_rules[stage]`. +- `decision_rules::Vector`: one callable policy per stage. +- `uncertainty`: `(parameter, value)` pairs for ``w_t``. +- `state_in`: realized incoming state ``x_{t-1}``. +""" function apply_rule(stage::Int, decision_rules::Vector{T}, uncertainty, state_in) where {T} return apply_rule(stage, decision_rules[stage], uncertainty, state_in) end @@ -467,6 +700,32 @@ function simulate_multistage( ) end +""" + _set_multistage_parameters!(state_params_in, state_params_out, + uncertainties, states) -> Nothing + +Write MOI parameter values into a deterministic-equivalent JuMP model +across all ``T`` stages before solving. + +For each stage ``t = 1, \\ldots, T``: + +- **Initial state** (``t = 1`` only): `state_params_in[1]` receives + `states[1]` (the initial state ``x_0``). +- **Uncertainty** ``w_t``: each `(parameter, value)` pair in + `uncertainties[t]` is written. +- **Target** ``\\hat{x}_t``: the target parameters in + `state_params_out[t]` receive `states[t + 1]`. + +Note that `state_params_in[t]` for ``t > 1`` is NOT set here because in the +deterministic equivalent the incoming state is an internal variable linked +by constraints, not a parameter. + +# Arguments +- `state_params_in`: per-stage vectors of incoming-state parameters. +- `state_params_out`: per-stage vectors of `(target_parameter, state_variable)`. +- `uncertainties`: per-stage `(parameter, value)` pairs for ``w_t``. +- `states`: length-``(T+1)`` target trajectory ``[x_0, \\hat{x}_1, \\ldots, \\hat{x}_T]``. +""" function _set_multistage_parameters!( state_params_in, state_params_out, @@ -475,19 +734,20 @@ function _set_multistage_parameters!( ) for t in 1:length(state_params_in) state = states[t] - # Update state parameters in + # Only the initial state (t=1) is set as a parameter; later incoming + # states are internal variables in the deterministic equivalent. if t == 1 for (i, state_var) in enumerate(state_params_in[t]) set_parameter_value(state_var, state[i]) end end - # Update uncertainty + # Write sampled exogenous values into this stage's uncertainty parameters. for (uncertainty_param, uncertainty_value) in uncertainties[t] set_parameter_value(uncertainty_param, uncertainty_value) end - # Update state parameters out + # Write policy targets into this stage's output-state target parameters. for i in 1:length(state_params_out[t]) state_var = state_params_out[t][i][1] set_parameter_value(state_var, states[t + 1][i]) @@ -496,6 +756,26 @@ function _set_multistage_parameters!( return nothing end +""" + _simulate_multistage_det(det_equivalent, state_params_in, state_params_out, + uncertainties, states, integer_strategy) -> Float64 + +Solve the deterministic-equivalent model for a single uncertainty trajectory +and return the optimal objective. + +Sets all parameters via [`_set_multistage_parameters!`](@ref), solves the +coupled full-horizon problem ``Q(w; \\theta)`` through +[`with_sensitivity_solution`](@ref), and caches both the full objective and +the deficit-free operational cost in `det_equivalent.ext` for logging. + +# Arguments +- `det_equivalent::JuMP.Model`: full-horizon coupled JuMP model. +- `state_params_in`: per-stage incoming-state parameters. +- `state_params_out`: per-stage `(target_parameter, state_variable)` pairs. +- `uncertainties`: per-stage `(parameter, value)` pairs for ``w_t``. +- `states`: length-``(T+1)`` target trajectory from the policy. +- `integer_strategy::AbstractIntegerStrategy`: discrete-variable strategy. +""" function _simulate_multistage_det( det_equivalent::JuMP.Model, state_params_in, @@ -504,10 +784,13 @@ function _simulate_multistage_det( states, integer_strategy::AbstractIntegerStrategy, ) + # Write the full target trajectory and uncertainty into the DE model. _set_multistage_parameters!(state_params_in, state_params_out, uncertainties, states) return with_sensitivity_solution(det_equivalent, integer_strategy) do sensitivity_model + # Read the optimal objective value after solving. obj = objective_value(sensitivity_model) + # Cache both the full and deficit-free objectives for logging. sensitivity_model.ext[:_last_obj] = obj sensitivity_model.ext[:_last_obj_no_deficit] = get_objective_no_target_deficit(sensitivity_model) @@ -515,6 +798,42 @@ function _simulate_multistage_det( end end +""" + _simulate_multistage_det_with_parameter_duals(det_equivalent, state_params_in, + state_params_out, uncertainties, + states, integer_strategy) + -> (objective, Δ_states) + +Solve the deterministic equivalent and extract parameter duals for the +rrule pullback. + +Like [`_simulate_multistage_det`](@ref), this sets parameters and solves the +full-horizon problem. In addition it reads the dual sensitivities via +[`pdual`](@ref) for each stage: + +```math +\\Delta_{\\text{states}}[1] = \\frac{\\partial Q}{\\partial x_0}, \\qquad +\\Delta_{\\text{states}}[t+1] = \\lambda_t = \\frac{\\partial Q}{\\partial \\hat{x}_t}, +\\quad t = 1, \\ldots, T. +``` + +These ``\\lambda_t`` duals are the envelope-theorem gradient used in the +TS-DDR training objective (arXiv:2405.14973, Eq. 1.2). + +# Arguments +- `det_equivalent`: full-horizon coupled JuMP model. +- `state_params_in`: per-stage incoming-state parameters. +- `state_params_out`: per-stage `(target_parameter, state_variable)` pairs. +- `uncertainties`: per-stage `(parameter, value)` pairs for ``w_t``. +- `states`: length-``(T+1)`` target trajectory from the policy. +- `integer_strategy::AbstractIntegerStrategy`: discrete-variable strategy. + +# Returns +- `objective::Float64`: optimal coupled objective ``Q(w; \\theta)``. +- `Δ_states::Vector{Vector{Float64}}`: length-``(T+1)`` vector of parameter + duals; `Δ_states[1]` holds ``\\partial Q / \\partial x_0`` and + `Δ_states[t+1]` holds ``\\lambda_t``. +""" function _simulate_multistage_det_with_parameter_duals( det_equivalent, state_params_in, @@ -523,13 +842,18 @@ function _simulate_multistage_det_with_parameter_duals( states, integer_strategy::AbstractIntegerStrategy, ) + # Write the full target trajectory and uncertainty into the DE model. _set_multistage_parameters!(state_params_in, state_params_out, uncertainties, states) return with_sensitivity_solution(det_equivalent, integer_strategy) do sensitivity_model + # Read the optimal objective value after solving. objective = objective_value(sensitivity_model) + # Cache both the full and deficit-free objectives for logging. sensitivity_model.ext[:_last_obj] = objective sensitivity_model.ext[:_last_obj_no_deficit] = get_objective_no_target_deficit(sensitivity_model) + # Build the per-stage dual vector: initial-state duals at index 1, + # target-constraint duals lambda_t at index t+1. Δ_states = similar(states) Δ_states[1] = pdual.(state_params_in[1]) for t in 1:length(state_params_out) @@ -641,7 +965,9 @@ function ChainRulesCore.rrule( integer_strategy, ) pdual_available = true - catch + catch err + # Surface the reason the fast path was skipped without altering the fallback. + @debug "pdual path failed; falling back to DiffOpt reverse differentiation" exception = (err, catch_backtrace()) y = _simulate_stage( subproblem, state_param_in, @@ -791,7 +1117,9 @@ function ChainRulesCore.rrule( integer_strategy, ) pdual_available = true - catch + catch err + # Surface the reason the fast path was skipped without altering the fallback. + @debug "pdual path failed; falling back to DiffOpt reverse differentiation" exception = (err, catch_backtrace()) y = _simulate_multistage_det( det_equivalent, state_params_in, @@ -1084,30 +1412,52 @@ Q(\theta; w) = \sum_{t=1}^{T} q_t(x_{t-1}, w_t; \hat{x}_t), ``` -where each realized ``x_t`` is read from the previous stage solve. The gradient -therefore contains both the target duals ``\lambda_t`` and the sensitivity of -later realized states with respect to earlier targets. In the notation of the -extension note, +where each realized ``x_t`` is read from the previous stage solve. In this +single-shooting rollout ``\theta`` influences later stage costs through two +couplings: the realized-state chain (the solver map +``x_t = X_t(x_{t-1}, \hat{x}_t; w_t)`` propagates earlier targets forward), +and the **policy-feedback path** (the policy input at stage ``t`` is the +realized state ``x_{t-1}``, which itself depends on earlier targets). The exact +gradient is the total-derivative recursion ```math -\nabla_\theta Q(\theta; w) +\frac{d Q}{d \theta} = \sum_{t=1}^{T} \left[ - \frac{\partial q_t}{\partial \hat{x}_t} + \frac{\partial q_t}{\partial \hat{x}_t} \frac{d \hat{x}_t}{d \theta} + - \sum_{k=t+1}^{T} - \frac{\partial q_k}{\partial x_{k-1}} - \prod_{j=t+1}^{k-1} - \frac{\partial x_j}{\partial x_{j-1}} - \frac{\partial x_t}{\partial \hat{x}_t} -\right] -\nabla_\theta \pi_\theta(w_t, x_{t-1}). + \frac{\partial q_t}{\partial x_{t-1}} \frac{d x_{t-1}}{d \theta} +\right], +``` + +with the coupled state and target recursions (and ``d x_0 / d\theta = 0``) + +```math +\frac{d x_t}{d \theta} += +\frac{\partial X_t}{\partial x_{t-1}} \frac{d x_{t-1}}{d \theta} ++ +\frac{\partial X_t}{\partial \hat{x}_t} \frac{d \hat{x}_t}{d \theta}, +\qquad +\frac{d \hat{x}_t}{d \theta} += +\nabla_\theta \pi_\theta(w_t, x_{t-1}) ++ +\frac{\partial \pi_\theta}{\partial x_{t-1}} \frac{d x_{t-1}}{d \theta}. ``` -The dual terms come from target and transition constraints; the state -sensitivities are computed through DiffOpt in the rrules for -[`simulate_stage`](@ref) and [`get_next_state`](@ref). +All derivative products must be read as **total** derivatives that include the +policy-feedback composition: a term such as +``\partial \pi_\theta / \partial x_{k-1} \cdot d x_{k-1} / d\theta`` is present +at every stage, so ``\theta`` reaches stage ``k`` not only through the +realized-state chain ``\partial x_j / \partial x_{j-1}`` but also through the +policy input ``x_{k-1}``. Reverse-mode AD (Zygote through the stage rrules) +computes exactly this full chain: the dual terms from target and transition +constraints supply ``\partial q_t / \partial \hat{x}_t`` and +``\partial q_t / \partial x_{t-1}``, while the state sensitivities are computed +through DiffOpt in the rrules for [`simulate_stage`](@ref) and +[`get_next_state`](@ref). # Arguments - `model`: differentiable Flux-compatible policy. It receives @@ -1231,9 +1581,11 @@ function train_multistage( return objective end catch e - if handle_training_error(gradient_fallback, e, iter) - nothing - end + # handle_training_error rethrows for ErrorGradientFallback and logs a + # warning + returns true (skip this iteration) for ZeroGradientFallback; + # either way no gradient is available, so the try-expression is nothing. + handle_training_error(gradient_fallback, e, iter) + nothing end record(sample_log, iter, model) && break @@ -1248,19 +1600,6 @@ function train_multistage( return model end -function sim_states(t, m, initial_state, uncertainty_sample_vec, prev_states) - # Input: [uncertainty, previous_predicted_state] - # For t=1: return initial_state (no prediction needed) - # For t>1: policy receives [uncertainty[t-1], prev_states[t-1]] - if t == 1 - return Float32.(initial_state) - else - uncertainties_t = uncertainty_sample_vec[t - 1] - prev_state = prev_states[t - 1] - return m(vcat(uncertainties_t, prev_state)) - end -end - @doc raw""" train_multistage(model, initial_state, det_equivalent::JuMP.Model, state_params_in, state_params_out, uncertainty_sampler; @@ -1487,9 +1826,11 @@ function train_multistage( return objective end catch e - if handle_training_error(gradient_fallback, e, iter) - nothing - end + # handle_training_error rethrows for ErrorGradientFallback and logs a + # warning + returns true (skip this iteration) for ZeroGradientFallback; + # either way no gradient is available, so the try-expression is nothing. + handle_training_error(gradient_fallback, e, iter) + nothing end record(sample_log, iter, model) && break diff --git a/src/utils.jl b/src/utils.jl index 6dc07e5..5d6ee1c 100644 --- a/src/utils.jl +++ b/src/utils.jl @@ -27,21 +27,48 @@ end Create deficit variables to penalize state deviations in a JuMP model. -Supports three modes: -- L1 norm only: Uses `MOI.NormOneCone` (default if no penalty specified) -- L2 squared norm only: Uses sum of squared deviations (solver-compatible alternative to SecondOrderCone) -- Both norms: Creates both constraints with separate penalties +Supports three modes controlled by the penalty keywords. Let +``d \\in \\mathbb{R}^n`` be the deficit vector (`len = n`). + +**L1 norm only** (default when no penalty keyword is given, or `penalty_l1` +alone): + +```math +\\text{norm\\_deficit} \\geq \\| d \\|_1 = \\sum_{i=1}^{n} |d_i|, +\\quad \\text{objective} \\mathrel{+}= \\lambda_1 \\cdot \\text{norm\\_deficit}. +``` + +Implemented via `MOI.NormOneCone(1 + n)`. + +**L2 squared norm only** (`penalty_l2` alone): + +```math +\\text{norm\\_deficit} \\geq \\| d \\|_2^2 = \\sum_{i=1}^{n} d_i^2, +\\quad \\text{objective} \\mathrel{+}= \\lambda_2 \\cdot \\text{norm\\_deficit}. +``` + +**Both norms** (`penalty_l1` and `penalty_l2`): + +```math +\\text{norm\\_deficit} + \\geq \\lambda_1 \\| d \\|_1 + \\lambda_2 \\| d \\|_2^2, +\\quad \\text{objective} \\mathrel{+}= 1 \\cdot \\text{norm\\_deficit}. +``` # Arguments -- `model`: The JuMP model to add deficit variables to -- `len`: Number of deficit variables (typically dimension of state) -- `penalty_l1`: Penalty coefficient for L1 norm (NormOneCone). If `nothing` and L1 is used, defaults to max objective coefficient. -- `penalty_l2`: Penalty coefficient for L2 squared norm (sum of squares). If `nothing` and L2 is used, defaults to max objective coefficient. -- `penalty`: Legacy argument. If provided and penalty_l1/penalty_l2 are both `nothing`, uses this for L1 norm only. +- `model`: The JuMP model to add deficit variables to. +- `len`: Number of deficit variables (typically dimension of state). +- `penalty_l1`: Penalty coefficient ``\\lambda_1`` for the L1 norm + (`NormOneCone`). Pass `:auto` to use `max |objective coefficients|`. +- `penalty_l2`: Penalty coefficient ``\\lambda_2`` for the L2 squared norm + (sum of squares). Pass `:auto` to use `max |objective coefficients|`. +- `penalty`: Legacy argument. If provided and `penalty_l1`/`penalty_l2` are + both `nothing`, uses this for L1 norm only. # Returns -- `norm_deficit`: Single variable representing total penalized deviation (for logging compatibility) -- `_deficit`: Vector of deficit variables for each state dimension +- `norm_deficit`: Single variable representing total penalized deviation (for + logging compatibility). +- `_deficit`: Vector of deficit variables for each state dimension. # Examples ```julia @@ -381,6 +408,14 @@ function normalize_recur_state(state) end end +""" + (callback::SaveBest)(iter, model, loss) -> Bool + +Compare `loss` against the incumbent `callback.best_loss`. When `loss` is +strictly smaller, copy `model` to CPU, normalize recurrent state via +[`normalize_recur_state`](@ref), and write the Flux state to +`callback.model_path` with JLD2. Always returns `false` (never stops training). +""" function (callback::SaveBest)(iter, model, loss) if loss < callback.best_loss m = cpu(model) @@ -392,11 +427,36 @@ function (callback::SaveBest)(iter, model, loss) return false end +""" + StallingCriterium(patience::Int, best_loss::Float64, stall_count::Int) + +Early-stopping callback that halts training when the loss stalls. + +Tracks the number of consecutive iterations without improvement. When +`stall_count` reaches `patience`, the callback returns `true` to signal that +training should stop. Use `best_loss = Inf` and `stall_count = 0` for a fresh +start. + +# Arguments +- `patience::Int`: maximum consecutive non-improving iterations before stopping. +- `best_loss::Float64`: incumbent best loss. Use `Inf` to accept the first value. +- `stall_count::Int`: current stall counter (typically initialized to `0`). +""" mutable struct StallingCriterium <: Function patience::Int best_loss::Float64 stall_count::Int end + +""" + (callback::StallingCriterium)(iter, model, loss) -> Bool + +Update the stall counter and return `true` when `stall_count >= patience`. + +If `loss < best_loss`, reset the counter to zero and update the incumbent. +Otherwise increment `stall_count`. Returns `true` (stop training) once the +patience budget is exhausted; `false` otherwise. +""" function (callback::StallingCriterium)(iter, model, loss) if loss < callback.best_loss callback.best_loss = loss @@ -448,9 +508,25 @@ function Base.empty!(sample_log::SampleLog) return sample_log end +""" + _reset_sample_log!(sample_log::SampleLog) -> SampleLog + _reset_sample_log!(sample_log) -> typeof(sample_log) + +Clear the per-batch cache of a [`SampleLog`](@ref) before the next training +batch. For a `SampleLog`, delegates to `Base.empty!`; for any other type the +call is a no-op and returns `sample_log` unchanged. +""" _reset_sample_log!(sample_log::SampleLog) = empty!(sample_log) _reset_sample_log!(sample_log) = sample_log +""" + _total_objective_value(model::JuMP.Model) -> Float64 + _total_objective_value(models::Vector{JuMP.Model}) -> Float64 + +Return the objective value of `model`, falling back to the cached value +`model.ext[:_last_obj]` (default `0.0`) when the model is dirty or +`objective_value` throws. The multi-model method sums across all models. +""" function _total_objective_value(model::JuMP.Model) if model.is_model_dirty return get(model.ext, :_last_obj, 0.0) @@ -476,6 +552,13 @@ function (sample_log::SampleLog)(s::Int, models) return nothing end +""" + _sequential_mean(values) -> Float64 + +Compute the arithmetic mean of `values` using sequential accumulation (left +fold). This preserves the same floating-point summation order as the historical +`loss += ...; loss /= n` pattern inside the training loops. +""" # Sequential accumulation keeps the same floating-point summation order as the # historical `loss += ...; loss /= n` pattern inside the training loops. function _sequential_mean(values) @@ -642,6 +725,19 @@ function RolloutEvaluation( ) end +""" + _simulate_multistage_target_feedback(subproblems, state_params_in, + state_params_out, initial_state, uncertainties, decision_rules, + integer_strategy) -> Float64 + +Run a stage-wise rollout where the **target** state (not the realized state) is +fed back into the policy at each stage, matching the deterministic-equivalent +target-generation semantics from [`simulate_states`](@ref). Each stage +subproblem is solved sequentially; the accumulated objective value (including +target-deficit penalties) is returned. + +Used by [`RolloutEvaluation`](@ref) when `policy_state == :target`. +""" function _simulate_multistage_target_feedback( subproblems::Vector{JuMP.Model}, state_params_in, @@ -684,6 +780,18 @@ function _simulate_multistage_target_feedback( return objective end +""" + (evaluation::RolloutEvaluation)(iter, model) -> Nothing + +Evaluate the policy on the held-out scenario set every `stride` iterations. + +On active iterations (`iter % stride == 0`), rolls `model` out over all fixed +scenarios using either closed-loop (`:realized`) or target-feedback (`:target`) +semantics. Prints `metrics/rollout_objective_no_deficit` and +`metrics/rollout_target_violation_share`, and caches the values in +`evaluation.last_objective_no_deficit` / `evaluation.last_violation_share`. +Scenarios that fail to solve are skipped with a warning if all fail. +""" function (evaluation::RolloutEvaluation)(iter, model) iter % evaluation.stride == 0 || return nothing total = 0.0 @@ -739,6 +847,13 @@ function (evaluation::RolloutEvaluation)(iter, model) return nothing end +""" + var_set_name!(src::JuMP.VariableRef, dest::JuMP.VariableRef, t::Int) -> Nothing + +Name `dest` after `src` with a `#t` stage suffix. If `src` has a JuMP name, the +result is `"#"`; otherwise the MOI variable index is used as fallback, +producing `"_[]#"`. +""" function var_set_name!(src::JuMP.VariableRef, dest::JuMP.VariableRef, t::Int) name = JuMP.name(src) if !isempty(name) @@ -751,6 +866,21 @@ function var_set_name!(src::JuMP.VariableRef, dest::JuMP.VariableRef, t::Int) end end +""" + add_child_model_vars!(model, subproblem, t, state_params_in, state_params_out, + initial_state, var_src_to_dest, skip_parameter_refs) + -> Dict{VariableRef,VariableRef} + +Copy decision variables from stage-`t` `subproblem` into the deterministic- +equivalent `model`, populating the source-to-destination mapping +`var_src_to_dest`. State-coupling variables (incoming parameters and outgoing +realized-state/target pairs) are handled specially: at `t == 1` they become +fresh parameters in `model`; at `t > 1` incoming state parameters are linked to +the previous stage's realized state variables. Each copied variable is renamed +via [`var_set_name!`](@ref) with a `#t` suffix. Mutates `state_params_in`, +`state_params_out`, `var_src_to_dest`, and `skip_parameter_refs` in place; +returns `var_src_to_dest`. +""" function add_child_model_vars!( model::JuMP.Model, subproblem::JuMP.Model, @@ -759,6 +889,7 @@ function add_child_model_vars!( state_params_out::Vector{Vector{Tuple{Any,VariableRef}}}, initial_state::Vector{Float64}, var_src_to_dest::Dict{VariableRef,VariableRef}, + skip_parameter_refs::Set{VariableRef}, ) allvars = all_variables(subproblem) allvars = setdiff(allvars, state_params_in[t]) @@ -799,23 +930,30 @@ function add_child_model_vars!( for (i, src) in enumerate(state_params_in[t]) if src isa VariableRef var_src_to_dest[src] = state_params_out[t - 1][i][2] + push!(skip_parameter_refs, src) end state_params_in[t][i] = state_params_out[t - 1][i][2] - # delete parameter constraint associated with src - if src isa VariableRef - for con in - JuMP.all_constraints(subproblem, VariableRef, MOI.Parameter{Float64}) - obj = JuMP.constraint_object(con) - if obj.func == src - JuMP.delete(subproblem, con) - end - end - end end end return var_src_to_dest end +""" + copy_and_replace_variables(src, map::Dict{VariableRef,VariableRef}) + +Deep-copy a JuMP expression `src`, substituting every `VariableRef` key in +`map` with its destination value. Dispatches on the concrete expression type: + +- `Vector`: element-wise recursive call. +- `Real`: returned as-is (no variables to replace). +- `VariableRef`: direct lookup in `map`. +- `GenericAffExpr`: rebuild with remapped variable keys and same coefficients. +- `GenericQuadExpr`: rebuild affine part and remapped `UnorderedPair` keys. +- `GenericNonlinearExpr`: recursively remap arguments, then reconstruct via + `@expression`. + +Throws an error for unrecognized expression types. +""" function copy_and_replace_variables( src::Vector, map::Dict{JuMP.VariableRef,JuMP.VariableRef} ) @@ -875,6 +1013,18 @@ function copy_and_replace_variables(src::Any, ::Dict{JuMP.VariableRef,JuMP.Varia ) end +""" + create_constraint(model, obj, var_src_to_dest) -> ConstraintRef + +Add a constraint to `model` whose function is `obj.func` with all `VariableRef` +keys replaced via `var_src_to_dest` (see [`copy_and_replace_variables`](@ref)). + +Four methods handle different constraint types: +- Generic `ScalarConstraint`: uses `@constraint(model, new_func in obj.set)`. +- `ScalarConstraint{NonlinearExpr, MOI.EqualTo}`: `new_func == obj.set.value`. +- `ScalarConstraint{NonlinearExpr, MOI.LessThan}`: `new_func <= obj.set.upper`. +- `ScalarConstraint{NonlinearExpr, MOI.GreaterThan}`: `new_func >= obj.set.lower`. +""" function create_constraint(model, obj, var_src_to_dest) new_func = copy_and_replace_variables(obj.func, var_src_to_dest) return @constraint(model, new_func in obj.set) @@ -901,10 +1051,26 @@ function create_constraint( return @constraint(model, new_func >= obj.set.lower) end +""" + add_child_model_exps!(model, subproblem, var_src_to_dest, skip_parameter_refs, + state_params_out, state_params_in, t) -> Dict + +Copy all constraints and the objective contribution from stage-`t` `subproblem` +into the deterministic-equivalent `model`, remapping variables through +`var_src_to_dest` via [`create_constraint`](@ref) and +[`copy_and_replace_variables`](@ref). Constraint-based state parameters and +input parameters at `t == 1` are updated to point to the new model's +constraint refs. Parameter constraints for incoming states that are linked to +the previous realized state are skipped via `skip_parameter_refs`; all other +parameter constraints are copied. The subproblem objective is added to +`model`'s existing objective. Returns a `Dict` mapping source `ConstraintRef` +to destination `ConstraintRef`. +""" function add_child_model_exps!( model::JuMP.Model, subproblem::JuMP.Model, var_src_to_dest::Dict{VariableRef,VariableRef}, + skip_parameter_refs::Set{VariableRef}, state_params_out, state_params_in, t, @@ -914,6 +1080,11 @@ function add_child_model_exps!( cons_to_cons = Dict() for con in JuMP.all_constraints(subproblem; include_variable_in_set_constraints=true) #, F, S) obj = JuMP.constraint_object(con) + if obj.func isa VariableRef && + obj.set isa MOI.Parameter && + obj.func in skip_parameter_refs + continue + end c = create_constraint(model, obj, var_src_to_dest) cons_to_cons[con] = c if (state_params_out[t][1][1] isa ConstraintRef) @@ -961,6 +1132,9 @@ stage `t` with the incoming state parameter of stage `t+1`. Returns `(model, uncertainties_new)` where `uncertainties_new` has the same format as the input but with variable refs remapped to the deterministic-equivalent model. +This function also remaps `state_params_in` and `state_params_out` in place. +Copy those arrays before calling if you need to keep the original stage-wise +references for rollout evaluation or later model construction. """ function deterministic_equivalent!( model::JuMP.Model, @@ -972,6 +1146,7 @@ function deterministic_equivalent!( ) set_objective_sense(model, objective_sense(subproblems[1])) var_src_to_dest = Dict{VariableRef,VariableRef}() + skip_parameter_refs = Set{VariableRef}() for t in 1:length(subproblems) DecisionRules.add_child_model_vars!( model, @@ -981,13 +1156,20 @@ function deterministic_equivalent!( state_params_out, initial_state, var_src_to_dest, + skip_parameter_refs, ) end cons_to_cons = Vector{Dict}(undef, length(subproblems)) for t in 1:length(subproblems) cons_to_cons[t] = DecisionRules.add_child_model_exps!( - model, subproblems[t], var_src_to_dest, state_params_out, state_params_in, t + model, + subproblems[t], + var_src_to_dest, + skip_parameter_refs, + state_params_out, + state_params_in, + t, ) end @@ -1039,6 +1221,14 @@ function _remap_uncertainties( ] end +""" + find_variables(model::JuMP.Model, variable_name_parts::Vector{<:AbstractString}) + +Return variables from `model` whose JuMP name contains **all** substrings in +`variable_name_parts`. When the initial filter yields more than one variable, +results are reordered by matching `"[i]"` for `i = 1, 2, ...` to +produce a consistently indexed vector. +""" function find_variables(model::JuMP.Model, variable_name_parts::Vector{S}) where {S} all_vars = all_variables(model) interest_vars = all_vars[findall( diff --git a/test/runtests.jl b/test/runtests.jl index 84814a4..016e25d 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -317,6 +317,13 @@ include("test_score_function.jl") uncertainty_samples = [[(uncertainty_1, [2.0])], [(uncertainty_2, [1.0])]] initial_state = [5.0] + n_param_cons_before = length( + JuMP.all_constraints(subproblem2, VariableRef, MOI.Parameter{Float64}) + ) + has_state_in_param_before = any( + con -> JuMP.constraint_object(con).func == state_in_2, + JuMP.all_constraints(subproblem2, VariableRef, MOI.Parameter{Float64}), + ) det_equivalent, uncertainty_samples = DecisionRules.deterministic_equivalent!( quiet_nonlinear_ipopt_model(), subproblems, @@ -325,6 +332,14 @@ include("test_score_function.jl") initial_state, uncertainty_samples, ) + @test length( + JuMP.all_constraints(subproblem2, VariableRef, MOI.Parameter{Float64}) + ) == n_param_cons_before + @test has_state_in_param_before + @test any( + con -> JuMP.constraint_object(con).func == state_in_2, + JuMP.all_constraints(subproblem2, VariableRef, MOI.Parameter{Float64}), + ) obj_val = DecisionRules.simulate_multistage( det_equivalent, @@ -448,6 +463,26 @@ include("test_score_function.jl") optimize!(model5) @test compute_parameter_dual(model5, state_in5) ≈ -30.0 rtol=1.0e-1 @test compute_parameter_dual(model5, state_out5) ≈ 30.0 rtol=1.0e-1 + + model6 = Model() + @variable(model6, x6) + @variable(model6, p6 in MOI.Parameter(1.0)) + @constraint(model6, x6 - p6 >= 0) + @objective(model6, Min, x6) + @test_throws Exception compute_parameter_dual(model6, p6) + + # Test 7: documented docstring example (MIN sense) + # min 3x + p s.t. x >= 2*p, x >= 0 + # At optimality x* = 2p, so the objective is 7p and ∂obj/∂p = 7. + # The constraint normalizes to x - 2p >= 0 (coef of p is -2), dual = 3: + # contribution -(-2) * 3 = 6, plus the objective coefficient 1 → 7. + model7 = quiet_ipopt_model() + @variable(model7, x7 >= 0) + @variable(model7, p7 in MOI.Parameter(1.0)) + @constraint(model7, con7, x7 >= 2 * p7) + @objective(model7, Min, 3 * x7 + p7) + optimize!(model7) + @test compute_parameter_dual(model7, p7) ≈ 7.0 rtol=1.0e-2 end @testset "create_deficit!" begin @@ -1040,6 +1075,19 @@ include("test_score_function.jl") @test policy.n_uncertainty == n_uncertainty @test policy.n_state == n_state + policy_deep_head = state_conditioned_policy( + n_uncertainty, + n_state, + n_output, + layers; + activation=sigmoid, + encoder_type=Flux.LSTM, + combiner_layers=[7, 5], + ) + @test policy_deep_head.combiner isa Flux.Chain + Flux.reset!(policy_deep_head) + @test length(policy_deep_head(rand(Float32, n_uncertainty + n_state))) == n_output + # Test forward pass Flux.reset!(policy) input = rand(Float32, n_uncertainty + n_state) @@ -2128,6 +2176,9 @@ include("test_score_function.jl") # Empty layers (single layer) m_empty = dense_multilayer_nn(3, 2, Int[]; activation=relu, dense=Dense) @test size(m_empty(rand(Float32, 3))) == (2,) + m_empty.weight .= -1 + m_empty.bias .= 0 + @test all(m_empty(ones(Float32, 3)) .== 0) # Empty layers LSTM m_empty_lstm = dense_multilayer_nn(3, 2, Int[]; dense=LSTM) @@ -2147,12 +2198,36 @@ include("test_score_function.jl") @testset "policy_input_dim" begin @test policy_input_dim(5, 3) == 8 @test policy_input_dim(0, 4) == 4 + @test policy_input_dim(5, 3, 2) == 10 uncertainty_samples = [[(nothing, [1.0, 2.0]), (nothing, [3.0])]] initial_state = [0.0, 0.0, 0.0] @test policy_input_dim(uncertainty_samples, initial_state) == 5 end + @testset "ContextualPolicy" begin + ctx = stage_phase_context(4; period=4) + @test size(ctx) == (3, 4) + @test ctx[:, 1] == context_at(ctx, 1) + @test_throws BoundsError context_at(ctx, 5) + + ctx2 = fill(Float32(2), 1, 4) + @test size(vcat_contexts(ctx, ctx2)) == (4, 4) + @test_throws ArgumentError vcat_contexts(ctx, fill(Float32(0), 1, 3)) + + seen = Vector{Float32}[] + inner = x -> (push!(seen, Float32.(x)); Float32[x[1] + x[end]]) + policy = ContextualPolicy(inner, Float32[10 20; 30 40]) + + @test policy(Float32[1, 2]) == Float32[12] + @test policy(Float32[3, 4]) == Float32[24] + @test seen[1] == Float32[10, 30, 1, 2] + @test seen[2] == Float32[20, 40, 3, 4] + Flux.reset!(policy) + @test policy.t == 0 + @test context_at(t -> Float32[t, t + 1], 3) == Float32[3, 4] + end + @testset "normalize_recur_state" begin plain = (a=1.0, b=[2.0, 3.0]) @test normalize_recur_state(plain) == plain @@ -2271,6 +2346,24 @@ include("test_score_function.jl") DecisionRules.get_objective_no_target_deficit(sp1) + DecisionRules.get_objective_no_target_deficit(sp2) @test total ≈ indiv + + quad_model = quiet_ipopt_model() + @variable(quad_model, x) + @variable(quad_model, norm_deficit >= 0) + @constraint(quad_model, x == 2) + @constraint(quad_model, norm_deficit == 3) + @objective(quad_model, Min, x^2 + 10 * norm_deficit) + optimize!(quad_model) + @test DecisionRules.get_objective_no_target_deficit(quad_model) ≈ 4.0 atol=1.0e-4 + + bad_quad = quiet_ipopt_model() + @variable(bad_quad, z) + @variable(bad_quad, norm_deficit_bad >= 0) + @constraint(bad_quad, z == 1) + @constraint(bad_quad, norm_deficit_bad == 2) + @objective(bad_quad, Min, z^2 + norm_deficit_bad^2) + optimize!(bad_quad) + @test_throws ErrorException DecisionRules.get_objective_no_target_deficit(bad_quad) end @testset "materialize_tangent edge cases" begin