This repository contains the code implementation of the experiments presented in the paper Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow.
- Use the code in meow/toy to reproduce the experimental results presented in Section 4.1 of our paper.
- Use the code in meow/cleanrl to reproduce the experimental results presented in Section 4.2 of our paper.
- Use the code in meow/skrl to reproduce the experimental results presented in Section 4.3 of our paper.
- Use the code in meow/plot to reproduce the figures presented in our paper.
To maintain reproducibility, we freezed the released versions of following repositories and list their licenses as follows:
- Toni-SM/skrl (at commit 631613a) is licensed under the MIT License.
- vwxyzjn/cleanrl (at commit 8cbca61) is licensed under the MIT License.
- VincentStimper/normalizing-flows (at commit 848277e) is licensed under the MIT License.
- rail-berkeley/softlearning (at commit 13cf187) is licensed under the MIT License.
Further changes based on the repository above are licensed under the MIT License.
If you find this repository useful, please consider citing our paper:
@inproceedings{chao2024maximum,
title={Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow},
author={Chao, Chen-Hao and Feng, Chien and Sun, Wei-Fang and Lee, Cheng-Kuang and See, Simon and Lee, Chun-Yi},
booktitle={Proceedings of the International Conference on Neural Information Processing Systems (NeurIPS)},
year={2024}
}
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