A Survey on Self-play Methods in Reinforcement Learning
<|reference_start|>A Survey on Self-play Methods in Reinforcement Learning: 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.<|reference_end|>
@article{zhang2024a,
    title={A Survey on Self-play Methods in Reinforcement Learning},
    author={Ruize Zhang, Zelai Xu, Chengdong Ma, Chao Yu, Wei-Wei Tu, Shiyu Huang,
  Deheng Ye, Wenbo Ding, Yaodong Yang, Yu Wang},
    journal={arXiv preprint arXiv:2408.01072},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01072},
    primaryClass={cs.AI}
}