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https://github.com/WeiChengTseng/awesome-multi-agent

A curated list of awesome multi-agent learning papers
https://github.com/WeiChengTseng/awesome-multi-agent

awesome awesome-list communication imitation-learning inverse-reinforcement-learning multi-agent multi-agent-learning multi-agent-reinforcement-learning multi-agent-systems reinforcement-learning rl

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A curated list of awesome multi-agent learning papers

Host: GitHub
URL: https://github.com/WeiChengTseng/awesome-multi-agent
Owner: WeiChengTseng
License: mit
Created: 2021-12-20T03:39:58.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2022-01-10T00:25:38.000Z (over 2 years ago)
Last Synced: 2024-05-23T09:02:05.576Z (4 months ago)
Topics: awesome, awesome-list, communication, imitation-learning, inverse-reinforcement-learning, multi-agent, multi-agent-learning, multi-agent-reinforcement-learning, multi-agent-systems, reinforcement-learning, rl
Homepage:
Size: 15.6 KB
Stars: 39
Watchers: 3
Forks: 3
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

ultimate-awesome - awesome-multi-agent - A curated list of awesome multi-agent learning papers. (Other Lists / PowerShell Lists)

README

        # Awesome Multi-Agent Learning [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

Multi-Agent Learning is a very exciting research area, which has strong connections with single-agent RL, multi-agent systems, game theory, evolutionary computation, communication framework and adaptation.

This repo contains a set of research papers as well as related information. The papers are sorted by time. Any suggestions and pull requests are welcome.

## Overview

 - [Tutorials](#tutorials)

 - [Research Papers](#papers)

    - RL in multi-agent

    - Imitation Learning and Inverse RL

    - Offline Learning

    - Communication

    - Adaptation

    - Multi-Agent Influence

    - Others

 - [Environment](#environment)

## Tutorials

- **SJTU Multi-Agent Reinforcement Learning Tutorial** [[website]](http://wnzhang.net/tutorials/marl2018/index.html)

  - J. Wang, W. Zhang at SJTU 2018 

-  **Multiagent Learning: Foundations and Recent Trends** [[website]](http://www.cs.utexas.edu/~larg/ijcai17_tutorial/)

    - S. Albrecht, P. Stone, **IJCAI 2017**

- **COMP310: Multi Agent System** [[website]](https://cgi.csc.liv.ac.uk/~trp/COMP310.html)    

  - T. Payne, 2017-2018

- **CompSci 285: Multi-Agent Systems** [[website]](https://www.seas.harvard.edu/courses/cs285/CS_285/Course_Home.html)   

  - D. Parkes, 2013

- **CS 224M : Multi Agent Systems** [[website]](http://web.stanford.edu/class/cs224m/)

  - Y. Shoham, 2013-14

- **Videos for "An Introduction to Multiagent Systems (Second Edition)"** [[website]](http://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/imas/videos/)   

  - M. Wooldridge, John Wiley & Sons, 2009

## Papers

### RL in multi-agent

- **Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition** [[paper]](https://arxiv.org/abs/2105.08692)

  - B. Liu, Q. Liu, P. Stone, A. Garg, Y. Zhu, A. Anandkumar, **ICML 2021**

- **Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning** [[paper]](https://arxiv.org/abs/2006.04222)

  - S. Iqbal, C. A. Schroeder de Witt, B. Peng, W. Böhmer, S. Whiteson, F. Sha, **ICML 2021**

- **Deep Implicit Coordination Graphs for Multi-Agent Reinforcement Learning** [[paper]](https://arxiv.org/abs/2006.11438)

  - S. Li, J. K. Gupta, P. Morales, R. Allen, M. J. Kochenderfer, **AAMAS 2021**

- **Multi-Agent Game Abstraction via Graph Attention Neural Network** [[paper]](https://arxiv.org/abs/1911.10715)

  - Y. Liu, W. Wang, Y. Hu, J. Hao, X. Chen, Y. Gao, **AAAI 2020**

- **Actor-Attention-Critic for Multi-Agent Reinforcement Learning** [[paper]](https://arxiv.org/abs/1810.02912) [[code]](https://github.com/shariqiqbal2810/MAAC)

  - S. Iqbal, F. Sha, **ICML 2019**

- **QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning** [[paper]](https://arxiv.org/abs/1905.05408)

  - K. Son, D. Kim, W. J. Kang, D. E. Hostallero, Y. Yi, **ICML 2019**

- **QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning** [[arxiv]](https://arxiv.org/abs/1803.11485)

  - T. Rashid, M. Samvelyan, C. Witt, **ICML 2018**

- **Emergent Complexity via Multi-agent Competition** [[website]](https://arxiv.org/abs/1710.03748)[[Code]](https://github.com/openai/multiagent-competition)

  - T. Bansal, J. Pachocki, S. Sidor, **ICLR 2018**

- **Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments** [[paper]](https://arxiv.org/abs/1706.02275)[[code1]](https://github.com/openai/multiagent-particle-envs)[[code2]](https://github.com/openai/maddpg)

  - R. Lowe, Y. Wu, A. Tamar, **NeurIPS 2017**

### Competition Scenario

- **Emergent complexity through multi-agent competition** [[paper]](https://arxiv.org/pdf/1710.03748.pdf)

    - T. Bansal, J. Pachocki, S. Sidor, I. Sutskever, I. Mordatch, **ICLR 2018**

### Imitation Learning and Inverse RL

- **Multi-Agent Adversarial Inverse Reinforcement Learning** [[paper]](https://arxiv.org/abs/1907.13220)

  - L. Yu, J. Song, S. Ermon, **ICML 2019**

- **Multi-Agent Generative Adversarial Imitation Learning** [[paper]](https://arxiv.org/pdf/1807.09936.pdf)

  - J. Song, H. Ren, D. Sadigh, S. Ermon, **NeurIPS 2018**

### Offline Learning

-  **Offline Pre-trained Multi-Agent Decision Transformer** [[paper]](https://arxiv.org/abs/2112.02845)

    - L. Meng, M. Wen, Y. Yang, C. Le, X. Li, W. Zhang, Y. Wen, H. Zhang, J. Wang, B. Xu

### Communication

- **Multi-Agent Graph-Attention Communication and Teaming** [[paper]](https://dl.acm.org/doi/abs/10.5555/3463952.3464065) [[code]](https://github.com/MAGIC-AAMAS/MAGIC)

  - Y. Niu, R. Paleja, M. Gombolay, **AAMAS 2021**

- **TarMAC: Targeted Multi-Agent Communication** [[paper]](https://proceedings.mlr.press/v97/das19a.html)

  - A. Das, T. Gervet, J. Romoff, D. Batra, D. Parikh, M. Rabbat, J. Pineau, **ICML 2019**

- **Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks** [[paper]](https://arxiv.org/abs/1812.09755)

  - A. Singh, T. Jain, S. Sukhbaatar, **ICLR 2019**

- **Learning Attentional Communication for Multi-Agent Cooperation** [[paper]](https://arxiv.org/abs/1805.07733)

  - J. Jiang, Z. Lu, **NeurIPS 2018**

- **Learning to Communicate with Deep Multi-Agent Reinforcement Learning** [[paper]](https://proceedings.neurips.cc/paper/2016/hash/c7635bfd99248a2cdef8249ef7bfbef4-Abstract.html)

- **Learning Multiagent Communication with Backpropagation** [[paper]](https://arxiv.org/abs/1605.07736)

  - S. Sukhbaatar, A. Szlam, R. Fergus, **NeurIPS 2016**

### Adaptation

- **Meta-CPR: Generalize to Unseen Large Number of Agents with Communication Pattern Recognition Module** [[paper]](https://arxiv.org/abs/2112.07222)

  - W. C. Tseng, W. Wei, D. C. Juan, M. Sun, **pre-print**

- **A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning** [[paper]](https://arxiv.org/abs/2011.00382)

    - D. Kim, M. Liu, M. Riemer, C. Sun, M. Abdulhai, G. Habibi, S. Lopez-Cot, G. Tesauro, J. P. How, **ICML 2021**

- **Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning** [[paper]](https://arxiv.org/abs/2003.10423)

  - Q. Long, Z. Zhou, A. Gupta, F. Fang, Y. Wu, X. Wang, **ICLR 2020**

- **From Few to More: Largescale Dynamic Multiagent Curriculum Learning** [[paper]](https://arxiv.org/abs/1909.02790)

  - W. Wang, T. Yang, Y. Liu, J. Hao, X. Hao, Y. Hu, Y. Chen, C. Fan, Y. Gao, **AAAI 2020**

- **DiCE: The Infinitely Differentiable Monte Carlo Estimator** [[paper]](https://arxiv.org/abs/1802.05098)

    - J. Foerster, G. Farquhar, M. Al-Shedivat, T. Rocktäschel, E. P. Xing, S. Whiteson, **ICML 2018**

- **Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments** [[paper]](https://arxiv.org/pdf/1710.03641.pdf)

  - M. Al-Shedivat, T. Bansal, Y. Burda, I. Sutskever, I. Mordatch, P. Abbeel, **ICLR 2018**

### Multi-Agent Influence

- **Learning Latent Representations to Influence Multi-Agent Interaction** [[website]](https://sites.google.com/view/latent-strategies/) [[paper]](https://arxiv.org/abs/2011.06619)

  - A. Xie, D. P. Losey, R. Tolsma, C. Finn, D. Sadigh, **CoRL 2020**

- **Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning** [[paper]](https://arxiv.org/abs/1810.08647)

  - N. Jaques, A. Lazaridou, E. Hughes, C. Gulcehre, P. A. Ortega, DJ Strouse, J. Z. Leibo, N. de Freitas, **ICML 2019**

### Application

- **Distributed Heuristic Multi-Agent Path Finding with Communication** [[paper]](https://arxiv.org/abs/2106.11365)

  - Z. Ma, Y. Luo, H. Ma, **ICRA 2021**

### Others

- **Adaptable Agent Populations Using a Generative Model of Policies** [[paper]](https://arxiv.org/abs/2107.07506)

    - K. Derek, P. Isola, **NeurIPS 2021**

- **Emergent Tool Use From Multi-Agent Autocurricula** [[paper]](https://arxiv.org/abs/1909.07528)

    - B. Baker, I. Kanitscheider, T. Markov, Y. Wu, G. Powell, B. McGrew, I. Mordatch, **ICLR 2020**

## Environment

  

    

    

    

    

    

    

    

    

    

    

    

    

  

  

    

    Neural MMO

    

    

    SMAC

    

    

    Grid-World

    

    

    Unity MLAgent

    

  

  

    

    

    

    

    

    

    

    

    

    

    

    

  

  

    

    Hanabi Learning Environment

    

    

    MAgent

    

    

    multiagent-particle-envs

    

    

    multiagent-competition

    

  

  

  

    

    

    

  

  

    

    Multiagent emergence