[NeurIPS 2023] The official code for paper "State Regularized Policy Optimization on Data with Dynamics Shift".
[PDF] [Poster Page] [Openreview]
Follow the steps in OfflineRL
Download the files in Google Drive and change the path parameter in line:15 of examples/train_d4rl.py.
python examples/train_d4rl.py --algo_name=maple_st --exp_name=maple_st --seed 1 --task density_10,body_mass@walker2d-medium-expert-v0 --rew_reg_eta 0.1 --out_train_epoch 200 --device cuda:1
walker2d-medium-expert-v0 can be changed to other Offline RL environments. To run baseline algorithms, maple_st can be changed to maple, mopo, cql, etc.
If you find our code repository or paper useful, please cite with:
@article{xue2023state,
title={State Regularized Policy Optimization on Data with Dynamics Shift},
author={Xue, Zhenghai and Cai, Qingpeng and Liu, Shuchang and Zheng, Dong and Jiang, Peng and Gai, Kun and An, Bo},
journal={arXiv preprint arXiv:2306.03552},
year={2023}
}