RaghuHemadri / Multi-Agent-Reinforcement-Learning-Survey-Papers

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
• Review Papers
• Research Papers
  • Framework
  • Joint action learning
  • Cooperation and competition
  • Coordination
  • Security
  • Self-Play
  • Learning To Communicate
  • Transfer Learning
  • Imitation and Inverse Reinforcement Learning
  • Meta Learning
  • Application

Tutorial and Books

• Deep Multi-Agent Reinforcement Learning by Jakob N Foerster, 2018. PhD Thesis.
• Multi-Agent Machine Learning: A Reinforcement Approach by H. M. Schwartz, 2014.
• Multiagent Reinforcement Learning by Daan Bloembergen, Daniel Hennes, Michael Kaisers, Peter Vrancx. ECML, 2013.
• Multiagent systems: Algorithmic, game-theoretic, and logical foundations by Shoham Y, Leyton-Brown K. Cambridge University Press, 2008.

Review Papers

• Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms by Kaiqing Zhang, Zhuoran Yang, Tamer Başar. 2019.
• A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems by Silva, Felipe Leno da; Costa, Anna Helena Reali. JAIR, 2019.
• Autonomously Reusing Knowledge in Multiagent Reinforcement Learning by Silva, Felipe Leno da; Taylor, Matthew E.; Costa, Anna Helena Reali. IJCAI, 2018.
• Deep Reinforcement Learning Variants of Multi-Agent Learning Algorithms by Castaneda A O. 2016.
• Evolutionary Dynamics of Multi-Agent Learning: A Survey by Bloembergen, Daan, et al. JAIR, 2015.
• 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 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 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? by Shoham Y, Powers R, Grenager T. Artificial Intelligence, 2007.
• From single-agent to multi-agent reinforcement learning: Foundational concepts and methods by Neto G. Learning theory course, 2005.
• Evolutionary game theory and multi-agent reinforcement learning by Tuyls K, Nowé A. The Knowledge Engineering Review, 2005.
• 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 by Liviu Panait and Sean Luke, 2005.

Research Papers

Framework

• Mean Field Multi-Agent Reinforcement Learning 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 by Lowe R, Wu Y, Tamar A, et al. arXiv, 2017.
• Deep Decentralized Multi-task Multi-Agent RL under Partial Observability by Omidshafiei S, Pazis J, Amato C, et al. arXiv, 2017.
• Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games by Peng P, Yuan Q, Wen Y, et al. arXiv, 2017.
• Robust Adversarial Reinforcement Learning by Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta. arXiv, 2017.
• Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning by Foerster J, Nardelli N, Farquhar G, et al. arXiv, 2017.
• Multiagent reinforcement learning with sparse interactions by negotiation and knowledge transfer by Zhou L, Yang P, Chen C, et al. IEEE transactions on cybernetics, 2016.
• Decentralised multi-agent reinforcement learning for dynamic and uncertain environments by Marinescu A, Dusparic I, Taylor A, et al. arXiv, 2014.
• CLEANing the reward: counterfactual actions to remove exploratory action noise in multiagent learning by HolmesParker C, Taylor M E, Agogino A, et al. AAMAS, 2014.
• Bayesian reinforcement learning for multiagent systems with state uncertainty by Amato C, Oliehoek F A. MSDM Workshop, 2013.
• Multiagent learning: Basics, challenges, and prospects by Tuyls, Karl, and Gerhard Weiss. AI Magazine, 2012.
• Classes of multiagent q-learning dynamics with epsilon-greedy exploration by Wunder M, Littman M L, Babes M. ICML, 2010.
• Conditional random fields for multi-agent reinforcement learning by Zhang X, Aberdeen D, Vishwanathan S V N. ICML, 2007.
• Multi-agent reinforcement learning using strategies and voting by Partalas, Ioannis, Ioannis Feneris, and Ioannis Vlahavas. ICTAI, 2007.
• A reinforcement learning scheme for a partially-observable multi-agent game by Ishii S, Fujita H, Mitsutake M, et al. Machine Learning, 2005.
• Asymmetric multiagent reinforcement learning by Könönen V. Web Intelligence and Agent Systems, 2004.
• Adaptive policy gradient in multiagent learning by Banerjee B, Peng J. AAMAS, 2003.
• Reinforcement learning to play an optimal Nash equilibrium in team Markov games by Wang X, Sandholm T. NIPS, 2002.
• Multiagent learning using a variable learning rate by Michael Bowling and Manuela Veloso, 2002.
• Value-function reinforcement learning in Markov game by Littman M L. Cognitive Systems Research, 2001.
• Hierarchical multi-agent reinforcement learning 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 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 by Conitzer V, Sandholm T. Machine Learning, 2007.
• Extending Q-Learning to General Adaptive Multi-Agent Systems by Tesauro, Gerald. NIPS, 2003.
• Multiagent reinforcement learning: theoretical framework and an algorithm. by Hu, Junling, and Michael P. Wellman. ICML, 1998.
• The dynamics of reinforcement learning in cooperative multiagent systems by Claus C, Boutilier C. AAAI, 1998.
• Markov games as a framework for multi-agent reinforcement learning by Littman, Michael L. ICML, 1994.

Cooperation and competition

• Emergent complexity through multi-agent competition by Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch, 2018.
• Learning with opponent learning awareness by Jakob Foerster, Richard Y. Chen2, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch, 2018.
• Multi-agent Reinforcement Learning in Sequential Social Dilemmas by Leibo J Z, Zambaldi V, Lanctot M, et al. arXiv, 2017. [Post]
• Reinforcement Learning in Partially Observable Multiagent Settings: Monte Carlo Exploring Policies with PAC Bounds by Roi Ceren, Prashant Doshi, and Bikramjit Banerjee, pp. 530-538, AAMAS 2016.
• Opponent Modeling in Deep Reinforcement Learning by He H, Boyd-Graber J, Kwok K, et al. ICML, 2016.
• Multiagent cooperation and competition with deep reinforcement learning by Tampuu A, Matiisen T, Kodelja D, et al. arXiv, 2015.
• Emotional multiagent reinforcement learning in social dilemmas 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 by Bab, Avraham, and Ronen I. Brafman. Journal of Machine Learning Research, 2008.
• Combining policy search with planning in multi-agent cooperation by Ma J, Cameron S. Robot Soccer World Cup, 2008.
• Collaborative multiagent reinforcement learning by payoff propagation by Kok J R, Vlassis N. JMLR, 2006.
• Learning to cooperate in multi-agent social dilemmas by de Cote E M, Lazaric A, Restelli M. AAMAS, 2006.
• Learning to compete, compromise, and cooperate in repeated general-sum games by Crandall J W, Goodrich M A. ICML, 2005.
• Sparse cooperative Q-learning by Kok J R, Vlassis N. ICML, 2004.

Coordination

• Coordinated Multi-Agent Imitation Learning by Le H M, Yue Y, Carr P. arXiv, 2017.
• Reinforcement social learning of coordination in networked cooperative multiagent systems by Hao J, Huang D, Cai Y, et al. AAAI Workshop, 2014.
• Coordinating multi-agent reinforcement learning with limited communication by Zhang, Chongjie, and Victor Lesser. AAMAS, 2013.
• Coordination guided reinforcement learning by Lau Q P, Lee M L, Hsu W. AAMAS, 2012.
• Coordination in multiagent reinforcement learning: a Bayesian approach by Chalkiadakis G, Boutilier C. AAMAS, 2003.
• Coordinated reinforcement learning by Guestrin C, Lagoudakis M, Parr R. ICML, 2002.
• Reinforcement learning of coordination in cooperative multi-agent systems by Kapetanakis S, Kudenko D. AAAI/IAAI, 2002.

Security

• Markov Security Games: Learning in Spatial Security Problems 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 by Yasuyuki S, Hirofumi O, Tadashi M, et al. Control Conference (ASCC), 2015
• Improving learning and adaptation in security games by exploiting information asymmetry by He X, Dai H, Ning P. INFOCOM, 2015.

Self-Play

• A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning 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 by Heinrich, Johannes, and David Silver. arXiv, 2016.
• Fictitious Self-Play in Extensive-Form Games by Heinrich, Johannes, Marc Lanctot, and David Silver. ICML, 2015.

Learning To Communicate

• Emergent Communication through Negotiation 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 by Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls, Stephen Clark
• EMERGENCE OF LANGUAGE WITH MULTI-AGENT GAMES: LEARNING TO COMMUNICATE WITH SEQUENCES OF SYMBOLS by Serhii Havrylov, Ivan Titov. ICLR Workshop, 2017.
• Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning by Abhishek Das, Satwik Kottur, et al. arXiv, 2017.
• Emergence of Grounded Compositional Language in Multi-Agent Populations by Igor Mordatch, Pieter Abbeel. arXiv, 2017. [Post]
• Cooperation and communication in multiagent deep reinforcement learning by Hausknecht M J. 2017.
• Multi-agent cooperation and the emergence of (natural) language by Lazaridou A, Peysakhovich A, Baroni M. arXiv, 2016.
• Learning to communicate to solve riddles with deep distributed recurrent q-networks by Foerster J N, Assael Y M, de Freitas N, et al. arXiv, 2016.
• Learning to communicate with deep multi-agent reinforcement learning by Foerster J, Assael Y M, de Freitas N, et al. NIPS, 2016.
• Learning multiagent communication with backpropagation by Sukhbaatar S, Fergus R. NIPS, 2016.
• Efficient distributed reinforcement learning through agreement by Varshavskaya P, Kaelbling L P, Rus D. Distributed Autonomous Robotic Systems, 2009.

Transfer Learning

• Simultaneously Learning and Advising in Multiagent Reinforcement Learning by Silva, Felipe Leno da; Glatt, Ruben; and Costa, Anna Helena Reali. AAMAS, 2017.
• Accelerating Multiagent Reinforcement Learning through Transfer Learning by Silva, Felipe Leno da; and Costa, Anna Helena Reali. AAAI, 2017.
• Accelerating multi-agent reinforcement learning with dynamic co-learning by Garant D, da Silva B C, Lesser V, et al. Technical report, 2015
• Transfer learning in multi-agent systems through parallel transfer by Taylor, Adam, et al. ICML, 2013.
• Transfer learning in multi-agent reinforcement learning domains by Boutsioukis, Georgios, Ioannis Partalas, and Ioannis Vlahavas. European Workshop on Reinforcement Learning, 2011.
• Transfer Learning for Multi-agent Coordination 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 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 by Lantao Yu, Jiaming Song, Stefano Ermon. ICML 2019.
• Multi-Agent Generative Adversarial Imitation Learning by Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon. NeurIPS 2018.
• Cooperative inverse reinforcement learning 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 by Lin X, Beling P A, Cogill R. arXiv, 2014.
• Multi-agent inverse reinforcement learning for zero-sum games by Lin X, Beling P A, Cogill R. arXiv, 2014.
• Multi-robot inverse reinforcement learning under occlusion with interactions by Bogert K, Doshi P. AAMAS, 2014.
• Multi-agent inverse reinforcement learning by Natarajan S, Kunapuli G, Judah K, et al. ICMLA, 2010.

Meta Learning

• Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments by l-Shedivat, M. 2018.

Application

• MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence by Zheng L et al. NIPS 2017 & AAAI 2018 Demo. (Github Page)
• Collaborative Deep Reinforcement Learning for Joint Object Search by Kong X, Xin B, Wang Y, et al. arXiv, 2017.
• Multi-Agent Stochastic Simulation of Occupants for Building Simulation by Chapman J, Siebers P, Darren R. Building Simulation, 2017.
• Extending No-MASS: Multi-Agent Stochastic Simulation for Demand Response of residential appliances by Sancho-Tomás A, Chapman J, Sumner M, Darren R. Building Simulation, 2017.
• Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving by Shalev-Shwartz S, Shammah S, Shashua A. arXiv, 2016.
• Applying multi-agent reinforcement learning to watershed management by Mason, Karl, et al. Proceedings of the Adaptive and Learning Agents workshop at AAMAS, 2016.
• Crowd Simulation Via Multi-Agent Reinforcement Learning by Torrey L. AAAI, 2010.
• Traffic light control by multiagent reinforcement learning systems by Bakker, Bram, et al. Interactive Collaborative Information Systems, 2010.
• Multiagent reinforcement learning for urban traffic control using coordination graphs 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 by Lee J W, Jangmin O. DEXA, 2002.
• Multi-agent reinforcement learning for traffic light control by Wiering, Marco. ICML. 2000.