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Paper list of multi-agent reinforcement learning (MARL)
| Date | Stars |
|---|---|
| 2026-07-31 | 4860 |
| 2026-08-02 | 4860 |
| 2026-08-04 | 4861 |
| 2026-08-05 | 4862 |
| 2026-08-06 | 4862 |
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## Paper Collection of Multi-Agent Reinforcement Learning (MARL) Multi-Agent Reinforcement Learning is a very interesting research area, which has strong connections with single-agent RL, multi-agent systems, game theory, evolutionary computation and optimization theory, and its application in Large Language Models (LLMs) and Robotics. This is a collection of research and review papers of multi-agent reinforcement learning (MARL). The Papers are sorted by time. Any suggestions and pull requests are welcome. The sharing principle of these references here is for research. If any authors do not want their paper to be listed here, please feel free to contact us. ## Overview * [Tutorial](https://github.com/LantaoYu/MARL-Papers#tutorial-and-books) * [Review Papers](https://github.com/LantaoYu/MARL-Papers#review-papers) * [Research Papers](https://github.com/LantaoYu/MARL-Papers#research-papers) * [Framework](https://github.com/LantaoYu/MARL-Papers#framework) * [Joint action learning](https://github.com/LantaoYu/MARL-Papers#joint-action-learning) * [Cooperation and competition](https://github.com/LantaoYu/MARL-Papers#cooperation-and-competition) * [Coordination](https://github.com/LantaoYu/MARL-Papers#coordination) * [Security](https://github.com/LantaoYu/MARL-Papers#security) * [Self-Play](https://github.com/LantaoYu/MARL-Papers#self-play) * [Learning To Communicate](https://github.com/LantaoYu/MARL-Papers#learning-to-communicate) * [Transfer Learning](https://github.com/LantaoYu/MARL-Papers#transfer-learning) * [Imitation and Inverse Reinforcement Learning](https://github.com/LantaoYu/MARL-Papers#imitation-and-inverse-reinforcement-learning) * [Meta Learning](https://github.com/LantaoYu/MARL-Papers#meta-learning) * [Application](https://github.com/LantaoYu/MARL-Papers#application) * [Networked MARL (Decentralized Training Decentralized Execution)](https://github.com/LantaoYu/MARL-Papers#networked-MARL) * [MARL in LLMs (MARL in Large Language Models)](https://github.com/LantaoYu/MARL-Papers#framework) * [MARL in Robotics (MARL in Robotics)](https://github.com/LantaoYu/MARL-Papers#framework) ## Tutorial and Books * [Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com/download) by Stefano V. Albrecht, Filippos Christianos, Lukas Schäfer, 2023. * [Many-agent Reinforcement Learning](https://discovery.ucl.ac.uk/id/eprint/10124273/12/Yang_10124273_thesis_revised.pdf) by Yaodong Yang, 2021. PhD Thesis. * [Deep Multi-Agent Reinforcement Learning](https://ora.ox.ac.uk/objects/uuid:a55621b3-53c0-4e1b-ad1c-92438b57ffa4) by Jakob N Foerster, 2018. PhD Thesis. * [Multi-Agent Machine Learning: A Reinforcement Approach](https://onlinelibrary.wiley.com/doi/book/10.1002/9781118884614) by H. M. Schwartz, 2014. * [Multiagent Reinforcement Learning](http://www.ecmlpkdd2013.org/wp-content/uploads/2013/09/Multiagent-Reinforcement-Learning.pdf) by Daan Bloembergen, Daniel Hennes, Michael Kaisers, Peter Vrancx. ECML, 2013. * [Multiagent systems: Algorithmic, game-theoretic, and logical foundations](http://www.masfoundations.org/download.html) by Shoham Y, Leyton-Brown K. Cambridge University Press, 2008. ## Review Papers * [The Landscape of Agentic Reinforcement Learning for LLMs: A Survey](https://arxiv.org/abs/2509.02547) by Guibin Zhang, Hejia Geng, Xiaohang Yu, Zhenfei Yin, Zaibin Zhang, Zelin Tan, Heng Zhou, Zhongzhi Li, Xiangyuan Xue, Yijiang Li, Yifan Zhou, Yang Chen, Chen Zhang, Yutao Fan, Zihu Wang, Songtao Huang, Yue Liao, Hongru Wang, Mengyue Yang, Heng Ji, Michael Littman, Jun Wang, Shuicheng Yan, Philip Torr, and Lei Bai. 2025. [[GitHub](https://github.com/xhyumiracle/Awesome-AgenticLLM-RL-Papers)] * [Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects](https://arxiv.org/pdf/2203.10603.pdf) by Xihuai Wang, Zhicheng Zhang, and Weinan Zhang. 2022. * [An overview of multi-agent reinforcement learning from game theoretical perspective](https
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