1. A Survey on Self-play Methods in Reinforcement Learning
- Author
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Zhang, Ruize, Xu, Zelai, Ma, Chengdong, Yu, Chao, Tu, Wei-Wei, Huang, Shiyu, Ye, Deheng, Ding, Wenbo, Yang, Yaodong, and Wang, Yu
- Subjects
Computer Science - Artificial Intelligence - 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.
- Published
- 2024