(zhuan) Paper Collection of Multi-Agent Reinforcement Learning (MARL)

this blog from: https://github.com/LantaoYu/MARL-Papers

Paper Collection of Multi-Agent Reinforcement Learning (MARL)

This is a collection of research and review papers of multi-agent reinforcement learning (MARL). 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 me. 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
  • Security
  • Self-Play
  • Communication
  • Transfer Learning
  • Inverse Reinforcement Learning
  • Application

Tutorial

• Multi-Agent Reinforcement Learning by Daan Bloembergen. AAMAS, 2014.
• Multiagent Reinforcement Learning by Daan Bloembergen, Daniel Hennes, Michael Kaisers, Peter Vrancx. ECML, 2013.

Review Papers

• 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
• 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.

Research Papers

Framework

• 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.
• 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.
• 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.

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

• Multi-agent Reinforcement Learning in Sequential Social Dilemmas by Leibo J Z, Zambaldi V, Lanctot M, et al. arXiv, 2017.
• 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.
• 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.
• 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.

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

• 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.

Communication

• Emergence of Grounded Compositional Language in Multi-Agent Populations by Igor Mordatch, Pieter Abbeel. arXiv, 2017. [Post]
• 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.
• Coordinating multi-agent reinforcement learning with limited communication by Zhang, Chongjie, and Victor Lesser. AAMAS, 2013.

Transfer Learning

• Transfer Learning for Multiagent Reinforcement Learning Systems by da Silva, Felipe Leno, and Anna Helena Reali Costa. IJCAI, 2016.
• 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.

Inverse Reinforcement Learning

• 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.

Application

• 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.