The official implementation of Policy-conditioned Environment Models are More Generalizable.
Please view Our Project Page for more details.
Download and install the main code from Policy Conditioned Model.
git clone https://github.com/xionghuichen/policy-conditioned-model.git
cd policy-conditioned-model
pip install -e .
Install the OfflineRL-Kit.
cd ..
git clone https://github.com/yihaosun1124/OfflineRL-Kit.git
cd OfflineRL-Kit
pip install -e .
Train a policy-conditioned model:
python train_scripts/train_pcm.py
Evaluate policies within the policy-conditioned model:
python eval_scripts/eval_pcm.py
Perform offline policy selection based on the policy-conditioned model:
python eval_scripts/offline_policy_selection.py
Perform model predictive control using the policy-conditioned model:
python mpc/mpc_cem_by_pcm.py
If you use this implementation in your work, please cite us with the following:
@inproceedings{
Policy-Conditioned Model,
title={Policy-conditioned Environment Models are More Generalizable},
author={Ruifeng Chen and Xiong-Hui Chen and Yihao Sun and Siyuan Xiao and Minhui Li and Yang Yu},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=g9mYBdooPA}
}