Year: 2024
Type: preprint
Abstract: Self-play, a learning paradigm where agents iteratively refine their policies by interacting with historical or concurrent versions of themselves or other evolving agents, has shown remarkable success in solving complex non-cooperative multi-agent tasks. Despite its growing prominence in multi-agent reinforcement learning (MARL), such as Go, poker, and video games, a comprehensive and structured understanding of self-play remains lacking. This survey fills this gap by offering a comprehensive roadmap to the diverse landscape of self-play methods. We begin by introducing the necessary preliminaries, including the MARL 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 non-cooperative scenarios. Finally, the survey highlights open challenges and future research directions in self-play.
Source: arXiv (Cornell University)
Authors: Zhang, Ruize, Xu, Zelai, Ma, Chengdong, Yu, Chao, Tu, Wei-Wei, Tang, Wenhao, Huang, Shiyu, Ye, Deheng, Ding, Wenbo, Yang, Yaodong, Wang, Yu
Institutions: Peking University, 4Thparadigm (China), Tencent (China)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G2045798616, G5811851265
OpenAlex ID: W4401978632