The role of $\gamma$ in Offline RL

This is a pytorch implementation of discount factor in Offline RL on Datasets for Deep Data-Driven Reinforcement Learning (D4RL), the corresponding paper is On the Role of Discount Factor in Offline Reinforcement Learning.

Quick Start

For experiments on D4RL, our code is implemented based on TD3+BC, please click on the folder TD3+BC_gammma and then:

$ python3 main.py

For experiments on Toy Example, please click on the folder Toy and then:

$ python3 bcq.py

$ python3 dqn.py

Citing

If you find this open source release useful, please reference in your paper (it is our honor):

@inproceedings{hu2022role,
  title={On the role of discount factor in offline reinforcement learning},
  author={Hu, Hao and Yang, Yiqin and Zhao, Qianchuan and Zhang, Chongjie},
  booktitle={International Conference on Machine Learning},
  pages={9072--9098},
  year={2022},
  organization={PMLR}
}

Note

• If you have any questions, please contact me: yangyiqi19@mails.tsinghua.edu.cn.