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https://github.com/chuangyc/awesome-multiagent-learning

A curated list of multiagent learning and related area resources.
https://github.com/chuangyc/awesome-multiagent-learning

List: awesome-multiagent-learning

awesome multi-agent-learning multi-agent-reinforcement-learning multiagent-systems

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A curated list of multiagent learning and related area resources.

Host: GitHub
URL: https://github.com/chuangyc/awesome-multiagent-learning
Owner: chuangyc
Created: 2019-02-15T08:43:19.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2019-08-15T09:55:14.000Z (about 5 years ago)
Last Synced: 2024-08-02T00:19:47.939Z (3 months ago)
Topics: awesome, multi-agent-learning, multi-agent-reinforcement-learning, multiagent-systems
Size: 43 KB
Stars: 70
Watchers: 6
Forks: 12
Open Issues: 0
Metadata Files:
- Readme: README.md

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ultimate-awesome - awesome-multiagent-learning - A curated list of multiagent learning and related area resources. (Other Lists / PowerShell Lists)

README

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

A curated list of multiagent learning and related area resources.

Inspired by [MARL-Papers](https://github.com/LantaoYu/MARL-Papers) and [awesome-activity-prediction](https://github.com/chinancheng/awesome-activity-prediction). The papers are sorted by algorithms so far. 

## Contributing

Welcome to send me email([email protected]) or [Pull Request](https://github.com/chuangyc/awesome-multiagent-learning/pulls) to add links or remove your works. 

## Overview

 - [Textbooks](#textbooks)

 - [Tutorials](#tutorials)

 - [Review Papers](#review-papers)

 - [Research Papers](#research-papers)

 - [Platforms](#platforms)

## Textbooks

* **Multi-Agent Machine Learning: A Reinforcement Approach** [[Website]](https://www.wiley.com/en-us/Multi+Agent+Machine+Learning%3A+A+Reinforcement+Approach-p-9781118362082)

    * H. M. Schwartz, Wiley, 2014

* **多智能體機器學習：強化學習方法**

    * 霍華德 M.施瓦兹 著，連曉峰 譯(simplified chinese translation for the above book.)

* **Multiagent Systems** [[Website]](http://www.the-mas-book.info/)

    * G. Weiss, MIT Press, 2013, 2nd edition

* **Graph Theoretic Methods in Multi-Agent Networks** [[Website]](https://press.princeton.edu/titles/9230.html)

    * M. Mesbahi and M. Egerstedt, Princeton University Press, 2010

* **Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations** [[Website]](http://www.masfoundations.org/)

    * Y. Shoham, K. Leyton-Brown, Cambridge University Press, 2009

* **Distributed Control of Robotic Networks** [[Website]](http://www.coordinationbook.info/)

    * F. Bullo, J. Cortés, S. Martínez, Princeton University Press 2009

* **An Introduction to MultiAgent Systems** [[Website]](http://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/imas/IMAS2e.html)

    * M. Wooldridge, John Wiley & Sons, 2009

* **Algebraic Graph Theory** [[Website]](https://www.amazon.com/Algebraic-Graph-Theory-Graduate-Mathematics/dp/0387952209)

    * C. Godsil and G. Royle, Springer, 2001

* **Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence** [[Website]](https://www.amazon.com/Multiagent-Systems-Distributed-Artificial-Intelligence/dp/0262731312/ref=pd_sim_sbs_b_1)

    * G. Weiss, The MIT Press, 2000

## 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, IJCAI2017

* **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

## Review Papers

* **Multiagent learning: Basics, challenges, and prospects** [[pdf]](http://www.weiss-gerhard.info/publications/AI_MAGAZINE_2012_TuylsWeiss.pdf)

    * K. Tuyls, G. Weiss, AI Magazine2012

* **A comprehensive survey of multi-agent reinforcement learning** [[pdf]](http://www.dcsc.tudelft.nl/~bdeschutter/pub/rep/07_019.pdf)

    8 L. Bus¸oniu, R. Babuska, and B. De Schutter, IIEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews2008

* **Foundations of Multi-Agent Learning** [[Website]](https://dl.acm.org/citation.cfm?id=1248179)

    * R. Vohra, M. Wellman, AIJ2007 

* **Cooperative multi-agent learning: The state of the art.** [[pdf]](https://cs.gmu.edu/~eclab/papers/panait05cooperative.pdf)

    * L. Panait and S. Luke, AAMAS2005

* **Learning in Multiagent Systems: An Introduction from a Game-Theoretic Perspective**[[pdf]](https://arxiv.org/pdf/cs/0308030.pdf)    

    * J. Vidal, AAMAS2002

* **Learning in multi-agent systems** [[Website]](https://dl.acm.org/citation.cfm?id=975678) 

    * E. Alonso, M. D’Inverno, D. Kudenko, KER2001

## Research Papers

### Deep Reinforcement Learning    

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

    * T. Rashid, M. Samvelyan, C. Witt, ICML2018

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

    * T. Bansal, J. Pachocki, S. Sidor, ICLR2018

### Counterfactual Policy Gradient

* **Counterfactual Multi-Agent policy gradients** [[arxiv]](https://arxiv.org/pdf/1705.08926.pdf)

    * J. Foerster, G. Farquhar, S. Whiteson

* **Stabilising experience replay for deep Multi-Agent reinforcement learning** [[arxiv]](https://arxiv.org/pdf/1702.08887.pdf)

    * J. Foerster, N. Nardelli, S. Whiteson, ICML2017

* **Learning to communicate with deep multi-agent reinforcement learning** [[paper]](https://papers.nips.cc/paper/6042-learning-to-communicate-with-deep-multi-agent-reinforcement-learning.pdf)

    * J. Foerster, I. Assael, S. Whiteson, NeuralIPS2016

### LOLA

* **Learning with Opponent-Learning Awareness** [[paper]](https://arxiv.org/abs/1709.04326)

    * J. Foerster, R. Chen, M. Shedivat, Shimon Whiteson, AAMAS2018

### DRQN (Deep Recurrent Q Network)

* **Recurrent Deep Multiagent Q-Learning for Autonomous Brokers in Smart Grid** [[paper]](https://www.ijcai.org/proceedings/2018/0079.pdf)

    * Y. Yang, J. Hao, G. Strbac, IJCAI2018

* **Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability** [[arxiv]](https://arxiv.org/pdf/1703.06182.pdf)

    * S. Omidshafiei, J. Pazis, J. Vian, ICML2017

* **Deep Recurrent Q-Learning for Partially Observable MDPs** [[arxiv]](https://arxiv.org/pdf/1507.06527.pdf)

    * M. Hausknecht, P. Stone, AAAI2015

### DDPG(Deep Determinstic Policy Gradient)

* **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, NIPS2017 

    

### VDN (Value Decomposition Network)

* **Value-Decomposition Networks For Cooperative Multi-Agent Learning** [[arxiv]](https://arxiv.org/pdf/1706.05296.pdf)

    * P. Sunehag, G. Lever, T. Graepel, AAMAS2018

 

### Q-Learning

#### Factorized Q-Learning

* **Factorized Q-Learning for Large-Scale Multi-Agent Systems** [[arxiv]](https://arxiv.org/abs/1809.03738)

    * Y. Chen, M. Zhou, Y. Wen, AAAI2019

#### MFMARL

* **Mean Field Multi-Agent Reinforcement Learning** [[arxiv]](https://arxiv.org/abs/1802.05438v4)[[COde]](https://github.com/mlii/mfrl)

    * Y. Yang, R. Luo, M. Li, ICML2018

#### Fuzzy-Q

* **Fuzzy Q-learning** [[website]](https://ieeexplore.ieee.org/document/622790)

    * P. Glorennec, L. Jouffe, IFSC1997

#### Correlated-Q

* **Correlated Q-learning** [[pdf]](https://www.aaai.org/Papers/ICML/2003/ICML03-034.pdf)

    * A. Greenwald, K. Hall, ICML2003 

#### Nash-Q

* **Nash Q-learning for general-sum stochastic games** [[pdf]](http://www.jmlr.org/papers/volume4/hu03a/hu03a.pdf)

    * J. Hu, M. Wellman, JMLR2003

#### Friend or Foe-Q

* **Friend-or-foe Q-learning in general-sum games** [[pdf]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.589.8571&rep=rep1&type=pdf)

    * M. Littman, ICML2001

#### Minimax-Q

* **Markov games as a framework for multi-agent reinforcement learning** [[pdf]](https://www2.cs.duke.edu/courses/spring07/cps296.3/littman94markov.pdf)

    * M. Littman, ICML1994

### Joint action Learning

* **Reaching pareto-optimality in prisoner’s dilemma using conditional joint action learning** [[website]](https://link.springer.com/article/10.1007/s10458-007-0020-8)

    * D. Banerjee, S. Sen, AAMAS2007

* **The dynamics of reinforcement learning in cooperative multiagent systems** [[pdf]](https://www.aaai.org/Papers/AAAI/1998/AAAI98-106.pdf)

    * C. Claus, C. Boutilier, AAAI1998

### Policy Hill Climbing

* **Multiagent learning using a variable learning rate** [[pdf]](http://www.cs.cmu.edu/~mmv/papers/02aij-mike.pdf)

    * M. Bowling, M. Veloso, Artificial Intelligence2002

### Learning Automata

### Gradient Ascent

## Platforms

* **Hanabi Learning Environment** [[Code]](https://github.com/deepmind/hanabi-learning-environment)

* **MAgent** [[Code]](https://github.com/geek-ai/MAgent) 

* **multiagent-particle-envs** [[Code]](https://github.com/openai/multiagent-particle-envs)

* **multiagent-competition** [[Code]](https://github.com/openai/multiagent-competition)

### Code for Starcraft: Brood War

* **SAIDA** [[Website]](https://github.com/TeamSAIDA/SAIDA)

* **TorchCraft** [[Code]](https://github.com/TorchCraft/TorchCraft)

* **Locutus** [[Code]](https://github.com/bmnielsen/Locutus/)