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Multi-Agent-Reinforcement-Learning-Survey-Papers

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# Multi-Agent Reinforcement Learning Survey/Papers

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.

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.

## 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)

## Tutorial and Books
* [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
* [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://arxiv.org/pdf/1911.10635.pdf) by Kaiqing Zhang, Zhuoran Yang, Tamer Başar. 2019.
* [A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems](https://www.jair.org/index.php/jair/article/view/11396) by Silva, Felipe Leno da; Costa, Anna Helena Reali. JAIR, 2019.
* [Autonomously Reusing Knowledge in Multiagent Reinforcement Learning](https://www.ijcai.org/proceedings/2018/774) by Silva, Felipe Leno da; Taylor, Matthew E.; Costa, Anna Helena Reali. IJCAI, 2018.
* [Deep Reinforcement Learning Variants of Multi-Agent Learning Algorithms](https://project-archive.inf.ed.ac.uk/msc/20162091/msc_proj.pdf) by Castaneda A O. 2016.
* [Evolutionary Dynamics of Multi-Agent Learning: A Survey](https://www.jair.org/index.php/jair/article/view/10952) by Bloembergen, Daan, et al. JAIR, 2015.
* [Game theory and multi-agent reinforcement learning](https://www.researchgate.net/publication/269100101_Game_Theory_and_Multi-agent_Reinforcement_Learning) by Nowé A, Vrancx P, De Hauwere Y M. Reinforcement Learning. Springer Berlin Heidelberg, 2012.
* [Multi-agent reinforcement learning: An overview](http://www.dcsc.tudelft.nl/~bdeschutter/pub/rep/10_003.pdf) by Buşoniu L, Babuška R, De Schutter B. Innovations in multi-agent systems and applications-1. Springer Berlin Heidelberg, 2010
* [A comprehensive survey of multi-agent reinforcement learning](http://www.dcsc.tudelft.nl/~bdeschutter/pub/rep/07_019.pdf) by Busoniu L, Babuska R, De Schutter B. IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews, 2008
* [If multi-agent learning is the answer, what is the question?](http://robotics.stanford.edu/~shoham/www%20papers/LearningInMAS.pdf) by Shoham Y, Powers R, Grenager T. Artificial Intelligence, 2007.
* [From single-agent to multi-agent reinforcement learning: Foundational concepts and methods](http://users.isr.ist.utl.pt/~mtjspaan/readingGroup/learningNeto05.pdf) by Neto G. Learning theory course, 2005.
* [Evolutionary game theory and multi-agent reinforcement learning](https://pdfs.semanticscholar.org/bb9f/bee22eae2b47bbf304804a6ac07def1aecdb.pdf) by Tuyls K, Nowé A. The Knowledge Engineering Review, 2005.
* [An Overview of Cooperative and Competitive Multiagent Learning](https://www.researchgate.net/publication/221622801_An_Overview_of_Cooperative_and_Competitive_Multiagent_Learning) by Pieter Jan ’t HoenKarl TuylsLiviu PanaitSean LukeJ. A. La Poutré. AAMAS's workshop LAMAS, 2005.
* [Cooperative multi-agent learning: the state of the art](https://cs.gmu.edu/~eclab/papers/panait05cooperative.pdf) by Liviu Panait and Sean Luke, 2005.

## Research Papers

### Framework
* [Mean Field Multi-Agent Reinforcement Learning](https://arxiv.org/pdf/1802.05438.pdf) by Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang. ICML 2018.
* [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://arxiv.org/pdf/1706.02275.pdf) by Lowe R, Wu Y, Tamar A, et al. arXiv, 2017.
* [Deep Decentralized Multi-task Multi-Agent RL under Partial Observability](https://arxiv.org/pdf/1703.06182.pdf) by Omidshafiei S, Pazis J, Amato C, et al. arXiv, 2017.
* [Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games](https://arxiv.org/pdf/1703.10069.pdf) by Peng P, Yuan Q, Wen Y, et al. arXiv, 2017.
* [Robust Adversarial Reinforcement Learning](https://arxiv.org/pdf/1703.02702.pdf) by Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta. arXiv, 2017.
* [Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning](https://arxiv.org/pdf/1702.08887.pdf) by Foerster J, Nardelli N, Farquhar G, et al. arXiv, 2017.
* [Multiagent reinforcement learning with sparse interactions by negotiation and knowledge transfer](https://arxiv.org/pdf/1508.05328.pdf) by Zhou L, Yang P, Chen C, et al. IEEE transactions on cybernetics, 2016.
* [Decentralised multi-agent reinforcement learning for dynamic and uncertain environments](https://arxiv.org/pdf/1409.4561.pdf) by Marinescu A, Dusparic I, Taylor A, et al. arXiv, 2014.
* [CLEANing the reward: counterfactual actions to remove exploratory action noise in multiagent learning](http://irll.eecs.wsu.edu/wp-content/papercite-data/pdf/2014iat-holmesparker.pdf) by HolmesParker C, Taylor M E, Agogino A, et al. AAMAS, 2014.
* [Bayesian reinforcement learning for multiagent systems with state uncertainty](http://www.fransoliehoek.net/docs/Amato13MSDM.pdf) by Amato C, Oliehoek F A. MSDM Workshop, 2013.
* [Multiagent learning: Basics, challenges, and prospects](http://www.weiss-gerhard.info/publications/AI_MAGAZINE_2012_TuylsWeiss.pdf) by Tuyls, Karl, and Gerhard Weiss. AI Magazine, 2012.
* [Classes of multiagent q-learning dynamics with epsilon-greedy exploration](http://icml2010.haifa.il.ibm.com/papers/191.pdf) by Wunder M, Littman M L, Babes M. ICML, 2010.
* [Conditional random fields for multi-agent reinforcement learning](http://www.machinelearning.org/proceedings/icml2007/papers/89.pdf) by Zhang X, Aberdeen D, Vishwanathan S V N. ICML, 2007.
* [Multi-agent reinforcement learning using strategies and voting](http://ama.imag.fr/~partalas/partalasmarl.pdf) by Partalas, Ioannis, Ioannis Feneris, and Ioannis Vlahavas. ICTAI, 2007.
* [A reinforcement learning scheme for a partially-observable multi-agent game](https://pdfs.semanticscholar.org/57fb/ae00e17c0d798559ebab0e8f4267e032f41d.pdf) by Ishii S, Fujita H, Mitsutake M, et al. Machine Learning, 2005.
* [Asymmetric multiagent reinforcement learning](http://lib.tkk.fi/Diss/2004/isbn9512273594/article1.pdf) by Könönen V. Web Intelligence and Agent Systems, 2004.
* [Adaptive policy gradient in multiagent learning](http://dl.acm.org/citation.cfm?id=860686) by Banerjee B, Peng J. AAMAS, 2003.
* [Reinforcement learning to play an optimal Nash equilibrium in team Markov games](https://papers.nips.cc/paper/2171-reinforcement-learning-to-play-an-optimal-nash-equilibrium-in-team-markov-games.pdf) by Wang X, Sandholm T. NIPS, 2002.
* [Multiagent learning using a variable learning rate](https://www.sciencedirect.com/science/article/pii/S0004370202001212) by Michael Bowling and Manuela Veloso, 2002.
* [Value-function reinforcement learning in Markov game](http://www.sts.rpi.edu/~rsun/si-mal/article3.pdf) by Littman M L. Cognitive Systems Research, 2001.
* [Hierarchical multi-agent reinforcement learning](http://researchers.lille.inria.fr/~ghavamza/my_website/Publications_files/agents01.pdf) by Makar, Rajbala, Sridhar Mahadevan, and Mohammad Ghavamzadeh. The fifth international conference on Autonomous agents, 2001.
* [An analysis of stochastic game theory for multiagent reinforcement learning](https://www.cs.cmu.edu/~mmv/papers/00TR-mike.pdf) by Michael Bowling and Manuela Veloso, 2000.

### Joint action learning
* [AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents](http://www.cs.cmu.edu/~conitzer/awesomeML06.pdf) by Conitzer V, Sandholm T. Machine Learning, 2007.
* [Extending Q-Learning to General Adaptive Multi-Agent Systems](https://papers.nips.cc/paper/2503-extending-q-learning-to-general-adaptive-multi-agent-systems.pdf) by Tesauro, Gerald. NIPS, 2003.
* [Multiagent reinforcement learning: theoretical framework and an algorithm.](http://www.lirmm.fr/~jq/Cours/3cycle/module/HuWellman98icml.pdf) by Hu, Junling, and Michael P. Wellman. ICML, 1998.
* [The dynamics of reinforcement learning in cooperative multiagent systems](http://www.aaai.org/Papers/AAAI/1998/AAAI98-106.pdf) by Claus C, Boutilier C. AAAI, 1998.
* [Markov games as a framework for multi-agent reinforcement learning](https://www.cs.duke.edu/courses/spring07/cps296.3/littman94markov.pdf) by Littman, Michael L. ICML, 1994.

### Cooperation and competition
* [Emergent complexity through multi-agent competition](https://arxiv.org/pdf/1710.03748.pdf) by Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch, 2018.
* [Learning with opponent learning awareness](https://arxiv.org/pdf/1709.04326.pdf) by Jakob Foerster, Richard Y. Chen2, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch, 2018.
* [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://arxiv.org/pdf/1702.03037.pdf) by Leibo J Z, Zambaldi V, Lanctot M, et al. arXiv, 2017. [[Post](https://deepmind.com/blog/understanding-agent-cooperation/)]
* [Reinforcement Learning in Partially Observable Multiagent Settings: Monte Carlo Exploring Policies with PAC Bounds](http://orca.st.usm.edu/~banerjee/papers/p530-ceren.pdf) by Roi Ceren, Prashant Doshi, and Bikramjit Banerjee, pp. 530-538, AAMAS 2016.
* [Opponent Modeling in Deep Reinforcement Learning](http://www.umiacs.umd.edu/~hal/docs/daume16opponent.pdf) by He H, Boyd-Graber J, Kwok K, et al. ICML, 2016.
* [Multiagent cooperation and competition with deep reinforcement learning](https://arxiv.org/pdf/1511.08779.pdf) by Tampuu A, Matiisen T, Kodelja D, et al. arXiv, 2015.
* [Emotional multiagent reinforcement learning in social dilemmas](http://www.uow.edu.au/~fren/documents/EMR_2013.pdf) by Yu C, Zhang M, Ren F. International Conference on Principles and Practice of Multi-Agent Systems, 2013.
* [Multi-agent reinforcement learning in common interest and fixed sum stochastic games: An experimental study](http://www.jmlr.org/papers/volume9/bab08a/bab08a.pdf) by Bab, Avraham, and Ronen I. Brafman. Journal of Machine Learning Research, 2008.
* [Combining policy search with planning in multi-agent cooperation](https://pdfs.semanticscholar.org/5120/d9f2c738ad223e9f8f14cb3fd5612239a35c.pdf) by Ma J, Cameron S. Robot Soccer World Cup, 2008.
* [Collaborative multiagent reinforcement learning by payoff propagation](http://www.jmlr.org/papers/volume7/kok06a/kok06a.pdf) by Kok J R, Vlassis N. JMLR, 2006.
* [Learning to cooperate in multi-agent social dilemmas](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.107.335&rep=rep1&type=pdf) by de Cote E M, Lazaric A, Restelli M. AAMAS, 2006.
* [Learning to compete, compromise, and cooperate in repeated general-sum games](http://www.machinelearning.org/proceedings/icml2005/papers/021_Learning_CrandallGoodrich.pdf) by Crandall J W, Goodrich M A. ICML, 2005.
* [Sparse cooperative Q-learning](http://www.machinelearning.org/proceedings/icml2004/papers/267.pdf) by Kok J R, Vlassis N. ICML, 2004.

### Coordination
* [Coordinated Multi-Agent Imitation Learning](https://arxiv.org/pdf/1703.03121.pdf) by Le H M, Yue Y, Carr P. arXiv, 2017.
* [Reinforcement social learning of coordination in networked cooperative multiagent systems](http://mipc.inf.ed.ac.uk/2014/papers/mipc2014_hao_etal.pdf) by Hao J, Huang D, Cai Y, et al. AAAI Workshop, 2014.
* [Coordinating multi-agent reinforcement learning with limited communication](http://www.aamas-conference.org/Proceedings/aamas2013/docs/p1101.pdf) by Zhang, Chongjie, and Victor Lesser. AAMAS, 2013.
* [Coordination guided reinforcement learning](http://www.ifaamas.org/Proceedings/aamas2012/papers/1B_1.pdf) by Lau Q P, Lee M L, Hsu W. AAMAS, 2012.
* [Coordination in multiagent reinforcement learning: a Bayesian approach](https://www.cs.toronto.edu/~cebly/Papers/bayesMARL.pdf) by Chalkiadakis G, Boutilier C. AAMAS, 2003.
* [Coordinated reinforcement learning](https://users.cs.duke.edu/~parr/icml02.pdf) by Guestrin C, Lagoudakis M, Parr R. ICML, 2002.
* [Reinforcement learning of coordination in cooperative multi-agent systems](http://www.aaai.org/Papers/AAAI/2002/AAAI02-050.pdf) by Kapetanakis S, Kudenko D. AAAI/IAAI, 2002.

### Security
* [Markov Security Games: Learning in Spatial Security Problems](http://www.fransoliehoek.net/docs/Klima16LICMAS.pdf) by Klima R, Tuyls K, Oliehoek F. The Learning, Inference and Control of Multi-Agent Systems at NIPS, 2016.
* [Cooperative Capture by Multi-Agent using Reinforcement Learning, Application for Security Patrol Systems](http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7244682) by Yasuyuki S, Hirofumi O, Tadashi M, et al. Control Conference (ASCC), 2015
* [Improving learning and adaptation in security games by exploiting information asymmetry](http://www4.ncsu.edu/~hdai/infocom-2015-XH.pdf) by He X, Dai H, Ning P. INFOCOM, 2015.

### Self-Play
* [A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning](https://arxiv.org/pdf/1711.00832.pdf) by Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, Thore Graepel. NIPS 2017.
* [Deep reinforcement learning from self-play in imperfect-information games](https://arxiv.org/pdf/1603.01121.pdf) by Heinrich, Johannes, and David Silver. arXiv, 2016.
* [Fictitious Self-Play in Extensive-Form Games](http://jmlr.org/proceedings/papers/v37/heinrich15.pdf) by Heinrich, Johannes, Marc Lanctot, and David Silver. ICML, 2015.

### Learning To Communicate
* [Emergent Communication through Negotiation](https://openreview.net/pdf?id=Hk6WhagRW) by Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z Leibo, Karl Tuyls, Stephen Clark, 2018.
* [Emergence of Linguistic Communication From Referential Games with Symbolic and Pixel Input](https://openreview.net/pdf?id=HJGv1Z-AW) by Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls, Stephen Clark
* [EMERGENCE OF LANGUAGE WITH MULTI-AGENT GAMES: LEARNING TO COMMUNICATE WITH SEQUENCES OF SYMBOLS](https://openreview.net/pdf?id=SkaxnKEYg) by Serhii Havrylov, Ivan Titov. ICLR Workshop, 2017.
* [Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning](https://arxiv.org/pdf/1703.06585.pdf) by Abhishek Das, Satwik Kottur, et al. arXiv, 2017.
* [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://arxiv.org/pdf/1703.04908.pdf) by Igor Mordatch, Pieter Abbeel. arXiv, 2017. [[Post](https://openai.com/blog/learning-to-communicate/)]
* [Cooperation and communication in multiagent deep reinforcement learning](https://repositories.lib.utexas.edu/handle/2152/45681) by Hausknecht M J. 2017.
* [Multi-agent cooperation and the emergence of (natural) language](https://openreview.net/pdf?id=Hk8N3Sclg) by Lazaridou A, Peysakhovich A, Baroni M. arXiv, 2016.
* [Learning to communicate to solve riddles with deep distributed recurrent q-networks](https://arxiv.org/pdf/1602.02672.pdf) by Foerster J N, Assael Y M, de Freitas N, et al. arXiv, 2016.
* [Learning to communicate with deep multi-agent reinforcement learning](https://arxiv.org/pdf/1605.06676.pdf) by Foerster J, Assael Y M, de Freitas N, et al. NIPS, 2016.
* [Learning multiagent communication with backpropagation](http://papers.nips.cc/paper/6398-learning-multiagent-communication-with-backpropagation.pdf) by Sukhbaatar S, Fergus R. NIPS, 2016.
* [Efficient distributed reinforcement learning through agreement](http://people.csail.mit.edu/lpk/papers/dars08.pdf) by Varshavskaya P, Kaelbling L P, Rus D. Distributed Autonomous Robotic Systems, 2009.

### Transfer Learning
* [Simultaneously Learning and Advising in Multiagent Reinforcement Learning](http://www.ifaamas.org/Proceedings/aamas2017/pdfs/p1100.pdf) by Silva, Felipe Leno da; Glatt, Ruben; and Costa, Anna Helena Reali. AAMAS, 2017.
* [Accelerating Multiagent Reinforcement Learning through Transfer Learning](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14217/14005) by Silva, Felipe Leno da; and Costa, Anna Helena Reali. AAAI, 2017.
* [Accelerating multi-agent reinforcement learning with dynamic co-learning](https://web.cs.umass.edu/publication/docs/2015/UM-CS-2015-004.pdf) by Garant D, da Silva B C, Lesser V, et al. Technical report, 2015
* [Transfer learning in multi-agent systems through parallel transfer](https://www.scss.tcd.ie/~tayloral/res/papers/Taylor_ParallelTransferLearning_ICML_2013.pdf) by Taylor, Adam, et al. ICML, 2013.
* [Transfer learning in multi-agent reinforcement learning domains](https://ewrl.files.wordpress.com/2011/08/ewrl2011_submission_19.pdf) by Boutsioukis, Georgios, Ioannis Partalas, and Ioannis Vlahavas. European Workshop on Reinforcement Learning, 2011.
* [Transfer Learning for Multi-agent Coordination](https://ai.vub.ac.be/~ydehauwe/publications/ICAART2011_2.pdf) by Vrancx, Peter, Yann-Michaël De Hauwere, and Ann Nowé. ICAART, 2011.

### Imitation and Inverse Reinforcement Learning
* [On the Utility of Learning about Humans for Human-AI Coordination](https://arxiv.org/abs/1910.05789) by Micah Carroll, Rohin Shah, Mark K. Ho, Thomas L. Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca Dragan. NeurIPS 2019.
* [Multi-Agent Adversarial Inverse Reinforcement Learning](https://arxiv.org/abs/1907.13220) by Lantao Yu, Jiaming Song, Stefano Ermon. ICML 2019.
* [Multi-Agent Generative Adversarial Imitation Learning](https://papers.nips.cc/paper/7975-multi-agent-generative-adversarial-imitation-learning) by Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon. NeurIPS 2018.
* [Cooperative inverse reinforcement learning](http://papers.nips.cc/paper/6420-cooperative-inverse-reinforcement-learning.pdf) by Hadfield-Menell D, Russell S J, Abbeel P, et al. NIPS, 2016.
* [Comparison of Multi-agent and Single-agent Inverse Learning on a Simulated Soccer Example](https://arxiv.org/pdf/1403.6822.pdf) by Lin X, Beling P A, Cogill R. arXiv, 2014.
* [Multi-agent inverse reinforcement learning for zero-sum games](https://arxiv.org/pdf/1403.6508.pdf) by Lin X, Beling P A, Cogill R. arXiv, 2014.
* [Multi-robot inverse reinforcement learning under occlusion with interactions](http://aamas2014.lip6.fr/proceedings/aamas/p173.pdf) by Bogert K, Doshi P. AAMAS, 2014.
* [Multi-agent inverse reinforcement learning](http://homes.soic.indiana.edu/natarasr/Papers/mairl.pdf) by Natarajan S, Kunapuli G, Judah K, et al. ICMLA, 2010.

### Meta Learning
* [Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments](https://arxiv.org/pdf/1710.03641.pdf) by l-Shedivat, M. 2018.

### Application
* [MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence](https://arxiv.org/pdf/1712.00600.pdf) by Zheng L et al. NIPS 2017 & AAAI 2018 Demo. ([Github Page](https://github.com/geek-ai/MAgent))
* [Collaborative Deep Reinforcement Learning for Joint Object Search](https://arxiv.org/pdf/1702.05573.pdf) by Kong X, Xin B, Wang Y, et al. arXiv, 2017.
* [Multi-Agent Stochastic Simulation of Occupants for Building Simulation](http://www.ibpsa.org/proceedings/BS2017/BS2017_051.pdf) by Chapman J, Siebers P, Darren R. Building Simulation, 2017.
* [Extending No-MASS: Multi-Agent Stochastic Simulation for Demand Response of residential appliances](http://www.ibpsa.org/proceedings/BS2017/BS2017_056.pdf) by Sancho-Tomás A, Chapman J, Sumner M, Darren R. Building Simulation, 2017.
* [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://arxiv.org/pdf/1610.03295.pdf) by Shalev-Shwartz S, Shammah S, Shashua A. arXiv, 2016.
* [Applying multi-agent reinforcement learning to watershed management](https://www.researchgate.net/profile/Karl_Mason/publication/299416955_Applying_Multi-Agent_Reinforcement_Learning_to_Watershed_Management/links/56f545b908ae95e8b6d1d3ff.pdf) by Mason, Karl, et al. Proceedings of the Adaptive and Learning Agents workshop at AAMAS, 2016.
* [Crowd Simulation Via Multi-Agent Reinforcement Learning](http://www.aaai.org/ocs/index.php/AIIDE/AIIDE10/paper/viewFile/2112/2550) by Torrey L. AAAI, 2010.
* [Traffic light control by multiagent reinforcement learning systems](https://pdfs.semanticscholar.org/61bc/b98b7ae3df894f4f72aba3d145bd48ca2cd5.pdf) by Bakker, Bram, et al. Interactive Collaborative Information Systems, 2010.
* [Multiagent reinforcement learning for urban traffic control using coordination graphs](https://staff.science.uva.nl/s.a.whiteson/pubs/kuyerecml08.pdf) by Kuyer, Lior, et al. oint European Conference on Machine Learning and Knowledge Discovery in Databases, 2008.
* [A multi-agent Q-learning framework for optimizing stock trading systems](https://www.researchgate.net/publication/221465347_A_Multi-agent_Q-learning_Framework_for_Optimizing_Stock_Trading_Systems) by Lee J W, Jangmin O. DEXA, 2002.
* [Multi-agent reinforcement learning for traffic light control](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=422747CB9AF552CF1C4E455220E3F96F?doi=10.1.1.32.9887&rep=rep1&type=pdf) by Wiering, Marco. ICML. 2000.

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