A Survey on Self-play Methods in Reinforcement Learning

• URL: http://arxiv.org/abs/2408.01072v1
• Date: Fri, 2 Aug 2024 07:47:51 GMT
• Title: A Survey on Self-play Methods in Reinforcement Learning
• Authors: Ruize Zhang, Zelai Xu, Chengdong Ma, Chao Yu, Wei-Wei Tu, Shiyu Huang, Deheng Ye, Wenbo Ding, Yaodong Yang, Yu Wang,
• Abstract summary: Self-play, characterized by agents' interactions with copies or past versions of itself, has recently gained prominence in reinforcement learning. This paper first clarifies the preliminaries of self-play, including the multi-agent reinforcement learning framework and basic game theory concepts. It provides a unified framework and classifies existing self-play algorithms within this framework.
• Score: 30.17222344626277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-play, characterized by agents' interactions with copies or past versions of itself, has recently gained prominence in reinforcement learning. This paper first clarifies the preliminaries of self-play, including the multi-agent reinforcement learning framework and basic game theory concepts. Then it provides a unified framework and classifies existing self-play algorithms within this framework. Moreover, the paper bridges the gap between the algorithms and their practical implications by illustrating the role of self-play in different scenarios. Finally, the survey highlights open challenges and future research directions in self-play. This paper is an essential guide map for understanding the multifaceted landscape of self-play in RL.

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