A Survey on Self-play Methods in Reinforcement Learning
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917022980374528 |
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| author | Zhang, Ruize Xu, Zelai Ma, Chengdong Yu, Chao Tu, Wei-Wei Tang, Wenhao Huang, Shiyu Ye, Deheng Ding, Wenbo Yang, Yaodong Wang, Yu |
| author_facet | Zhang, Ruize Xu, Zelai Ma, Chengdong Yu, Chao Tu, Wei-Wei Tang, Wenhao Huang, Shiyu Ye, Deheng Ding, Wenbo Yang, Yaodong Wang, Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01072 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Self-play Methods in Reinforcement Learning Zhang, Ruize Xu, Zelai Ma, Chengdong Yu, Chao Tu, Wei-Wei Tang, Wenhao Huang, Shiyu Ye, Deheng Ding, Wenbo Yang, Yaodong Wang, Yu Artificial Intelligence 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. |
| title | A Survey on Self-play Methods in Reinforcement Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2408.01072 |