This is a repository for the paper "Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models".
In this paper, we focus on methods that can learn from offline trajectories without reward annotations. We test methods ranging from RL to control, and find that planning with a learned latent dynamics model (PLDM) is a promising approach for this setting when the data is imperfect.
git clone git@github.com:vladisai/PLDM.git
cd PLDM
pip install -r requirements.txt
pip install -e .
- Go to
pldm_envs/, follow instructions to set up dataset for the environment of your hoice - Go to
pldm/, follow instruction to run training or evaluation
To see the datasets we used to train our models, see folders inside pldm_envs/.
The readmes there will guide you on how to download and set up the datasets.