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A Survey on Self-play Methods in Reinforcement Learning

2024-08-02 · Ruize Zhang, Zelai Xu, Chengdong Ma, Chao Yu, Wei-Wei Tu, Wenhao Tang, Shiyu Huang, Deheng Ye, Wenbo Ding, Yaodong Yang, Yu Wang

Self-play, characterized by agents' interactions with copies or past versions of themselves, has recently gained prominence in reinforcement learning (RL). 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.

📄 PDF Abstract BibTeX arXiv:2408.01072

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Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Survey

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey

2025-04-20 · Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer, Muhammad Awais Khan Bangash 외

This survey explores the development of meta-thinking capabilities in Large Language Models (LLMs) from a Multi-Agent Reinforcement Learning (MARL) perspective. Meta-thinking self-reflection, assessment, and control of t…

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Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning

2026-04-08 · Rohan Surana, Gagan Mundada, Xunyi Jiang, Chuhan Wang 외 arxiv

Reinforcement learning (RL) has become a central post-training tool for improving the reasoning abilities of large language models (LLMs). In these systems, the rollout, the trajectory sampled from a prompt to terminatio…

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A Brief Survey of Deep Reinforcement Learning

2017-08-19 · Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills

2026-08-03 · Gaytri Jena, Kapil Wanaskar, Vinija Jain, Aman Chadha 외 hf

Robot learning is splitting into two bets: policies that bake competence into frozen weights (vision-language-action, or VLA, models), and agents that write and refine their own executable skills as code. This survey org…

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Provable Self-Play Algorithms for Competitive Reinforcement Learning

2020-02-10 · ICML 2020 1 · Yu Bai, Chi Jin

Self-play, where the algorithm learns by playing against itself without requiring any direct supervision, has become the new weapon in modern Reinforcement Learning (RL) for achieving superhuman performance in practice. …

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)