PCBWorld

A reinforcement-learning environment for PCB routing, built on KiCad's interactive router.

Version	v1.1.1
KiCad	9.0.8 (via the engine submodule)
Python	3.12+
Platform	Linux x86_64 (primary), macOS
License	BSD-3-Clause; the engine is GPLv3 (see Licensing below)

PCBWorld wraps KiCad's push-and-shove router as a Gymnasium environment. An agent places tracks and vias on a real .kicad_pcb board, and every step is scored by KiCad's own design-rule checker. What routes here routes in KiCad.

The repository ships the environment, synthetic board generators, the real-board benchmark splits, one evaluation pipeline shared by every method, and three families of baselines: a decoder-only PPO/GRPO transformer, LLM tool-calling agents, and the rule-based routers FreeRouting and KRT.

Demo

An LLM agent routing two production boards end to end (time-lapse; click for the full video):

0018_hy_adapter	0100_smt-zvs-driver

All supplementary videos: PCBWorld_media/index.html.

Using the environment

from configs.loader.schema import EnvConfig
from pcb_world.core.env import PCBWorld
from pcb_world.core.action_schema import ACT_NET_SELECT, ACT_START_ROUTE, ACT_MAKE_LINE, ACT_NET_END

# EnvConfig: every env setting (defaults: configs/env.yaml)
env = PCBWorld("tests/fixtures/simple_routing_board.kicad_pcb", EnvConfig.with_overrides(max_steps=200))
obs, info = env.reset(seed=0)

src, dst = obs["board_static"]["nets"]["net_1"]["pads"].values()
(x0, y0), (x1, y1) = src["center"]["xy"], dst["center"]["xy"]

for action in (
    {"action_type": ACT_NET_SELECT, "net_id": 1},
    {"action_type": ACT_START_ROUTE, "x_mm": x0, "y_mm": y0, "layer": src["layer"]},
    {"action_type": ACT_MAKE_LINE, "x_mm": x1, "y_mm": y1, "routing_mode": 2},
    {"action_type": ACT_NET_END},
):
    obs, reward, terminated, truncated, info = env.step(action)

State, actions and reward: docs/ENVIRONMENT.md.

Quick start

1. Setup

git clone --recursive https://github.com/LGAI-Research/PCBWorld.git pcbworld && cd pcbworld
bash tools/setup/setup_all.sh        # conda env, pinned baselines, engine build, import smoke
conda activate pcbworld && export PYTHONPATH=build_rl/pcbnew/python/rl:. PCBWORLD_DATA_ROOT=$PWD/var/datasets

macOS first: brew install cmake ninja wxwidgets libgit2 protobuf ngspice libngspice pkgconf nng unixodbc.

2. Open-source boards (D3)

bash tools/quickstart/prepare_pcbench.sh --limit 30 --workers 8

Collects the PCBench boards and preprocesses them into the D3 benchmark set. Drop --limit for all of them.

3. Train an RL policy

bash tools/quickstart/train_rl.sh --data-dir var/datasets/synthetic/synth_2L_v2 --boards 200 --iterations 5 --seed 42

Generates the boards when the directory is empty, trains PPO on them, and evaluates the policy on the test split.

4. Route with an LLM

GEMINI_API_KEY=... bash tools/quickstart/run_llm.sh   # or OPENAI_API_KEY, ANTHROPIC_API_KEY, TOGETHER_API_KEY

Any one key works; the script picks the provider from it (Gemini has a free tier). Without a key it only prints the prompt and runs a single fixed step.

The shipped defaults use a newer potential-function combination than the paper. To reproduce the KDD submission with its original reward, follow experiments/kdd/.

Licensing

PCBWorld is two programs, distributed separately:

Program	Where	License
PCBWorld: the environment, agents, benchmark, training and evaluation code	this repository	BSD-3-Clause
PCBWorld Engine: the KiCad modifications, the RL router, the engine server	LGAI-Research/PCBWorld-Engine, pinned here as the engine/ submodule	GPLv3

Third-party notices: Notice.md.

Citation

Accepted to the KDD 2026 Workshop on Evaluation and Trustworthiness of Agentic AI (arXiv:2607.05915, PCBWorld.pdf).

@inproceedings{song2026pcbworld,
  title     = {PCBWorld: A Benchmark Environment for Engine-Grounded
               PCB Design Automation},
  author    = {Song, Hyungseok and Park, Junseok and Choi, Won-Seok and
               Bae, Seohui and Jeong, Han-Seul and Park, Youngjoon and
               Lee, Soonyoung},
  booktitle = {KDD 2026 Workshop on Evaluation and Trustworthiness of
               Agentic AI},
  year      = {2026},
  note      = {arXiv:2607.05915},
}

Contact

Open an issue, or write to hyungseok.song@lgresearch.ai. Bug reports and pull requests are welcome.