Efficient Multi-agent Reinforcement Learning by Planning

1 code implementation • 20 May 2024 • Qihan Liu, Jianing Ye, Xiaoteng Ma, Jun Yang, Bin Liang, Chongjie Zhang

Extensive experiments on the SMAC benchmark demonstrate that MAZero outperforms model-free approaches in terms of sample efficiency and provides comparable or better performance than existing model-based methods in terms of both sample and computational efficiency.

Computational Efficiency Model-based Reinforcement Learning +2

Learning Diverse Risk Preferences in Population-based Self-play

1 code implementation • 19 May 2023 • Yuhua Jiang, Qihan Liu, Xiaoteng Ma, Chenghao Li, Yiqin Yang, Jun Yang, Bin Liang, Qianchuan Zhao

In this paper, we aim to introduce diversity from the perspective that agents could have diverse risk preferences in the face of uncertainty.

Diversity reinforcement-learning +1

Offline Reinforcement Learning with Value-based Episodic Memory

1 code implementation • ICLR 2022 • Xiaoteng Ma, Yiqin Yang, Hao Hu, Qihan Liu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, Bin Liang

Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data.

D4RL Offline RL +2

Safe Opponent-Exploitation Subgame Refinement

no code implementations • 29 Sep 2021 • Mingyang Liu, Chengjie WU, Qihan Liu, Yansen Jing, Jun Yang, Pingzhong Tang, Chongjie Zhang

Search algorithms have been playing a vital role in the success of superhuman AI in both perfect information and imperfect information games.