Summary of A Survey on Self-play Methods in Reinforcement Learning, by Ruize Zhang et al.
A Survey on Self-play Methods in Reinforcement Learning
by Ruize Zhang, Zelai Xu, Chengdong Ma, Chao Yu, Wei-Wei Tu, Wenhao Tang, Shiyu Huang, Deheng Ye, Wenbo Ding, Yaodong Yang, Yu Wang
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a unified framework for self-play algorithms in reinforcement learning (RL), providing a comprehensive overview of existing approaches. It starts by reviewing the fundamentals of multi-agent RL and basic game theory concepts, before delving into the classification and analysis of various self-play methods. The authors also explore the practical implications of these algorithms in different scenarios, highlighting their potential applications and limitations. Finally, the paper identifies open challenges and future research directions in this rapidly evolving field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Self-play is a type of reinforcement learning where agents interact with copies or past versions of themselves. This paper explains what self-play is and how it works, including its uses in different situations. It’s like a guidebook for understanding the many ways self-play can be used in RL. The authors want to help people understand this complex topic better by providing a simple framework and explaining the pros and cons of each approach. |
Keywords
» Artificial intelligence » Classification » Reinforcement learning
