Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning

[NeurIPS 2024]

Zhishuai Liu · Pan Xu

Duke University

Official implementation of the paper "Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning", which is published in the Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS).

Installation instructions

Dependencies

• python == 3.7
• scipy == 1.7.3
• matplotlib == 2.2.3
• numpy == 1.21.6

Citation

@article{liu2024minimax,
  title={Minimax optimal and computationally efficient algorithms for distributionally robust offline reinforcement learning},
  author={Liu, Zhishuai and Xu, Pan},
  journal={Advances in Neural Information Processing Systems},
  year={2024}
}