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Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling stored state transitions.
Self-improving reactive agents based on reinforcement learning, planning and teaching
Long-H Lin. 1992 · 1992
Earlier work this paper cites.
Q-learning
Christopher JCH Watkins and Peter Dayan. 1992 · 1992
Earlier work this paper cites.
Multi-agent reinforcement learning: Independent vs. cooperative agents. In Proceedings of the tenth international conference on machine learning
Ming Tan. 1993 · 1993
Earlier work this paper cites.
Communication in reactive multiagent robotic systems
Tucker Balch and Ronald C Arkin. 1994 · 1994
Earlier work this paper cites.
A cooperative coevolutionary approach to function optimization. In International Conference on Parallel Problem Solving from Nature
Mitchell A Potter and Kenneth A De Jong. 1994 · 1994
Earlier work this paper cites.
Reinforcement learning: A survey
Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore. 1996 · 1996
Earlier work this paper cites.
Reinforcement learning: An introduction
Andrew Barto and Richard Sutton. 1998 · 1998
Earlier work this paper cites.
Similarity estimation techniques from rounding algorithms. In Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Moses S Charikar. 2002 · 2002
Earlier work this paper cites.
An analysis of cooperative coevolutionary algorithms
R Paul Wiegand. 2003 · 2003
Earlier work this paper cites.
Lenient learners in cooperative multiagent systems. In Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Liviu Panait, Keith Sullivan, and Sean Luke. 2006 · 2006
Earlier work this paper cites.
Hysteretic q-learning: an algorithm for decentralized reinforcement learning in cooperative multi-agent teams. In Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on
Laëtitia Matignon, Guillaume J Laurent, and Nadine Le Fort-Piat. 2007 · 2007
Earlier work this paper cites.
A comprehensive survey of multiagent reinforcement learning
Lucian Busoniu, Robert Babuska, and Bart De Schutter. 2008 · 2008
Earlier work this paper cites.
Theoretical advantages of lenient learners: An evolutionary game theoretic perspective
Liviu Panait, Karl Tuyls, and Sean Luke. 2008 · 2008
Earlier work this paper cites.
Multi-agent reinforcement learning: An overview
Lucian Buşoniu, Robert Babuška, and Bart De Schutter. 2010 · 2010
Cited alongside, same era.
Double Q-learning. In Advances in Neural Information Processing Systems
Hado Van Hasselt. 2010 · 2010
Cited alongside, same era.
Empirical and theoretical support for lenient learning. In The 10th International Conference on Autonomous Agents and Multiagent Systems-Volume 3
Daan Bloembergen, Michael Kaisers, and Karl Tuyls. 2011 · 2011
Cited alongside, same era.
Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems
Laetitia Matignon, Guillaume J Laurent, and Nadine Le Fort-Piat. 2012 · 2012
Cited alongside, same era.
Multiagent Learning: Basics, Challenges, and Prospects
Karl Tuyls and Gerhard Weiss. 2012 · 2012
Cited alongside, same era.
Multiagent-based reinforcement learning for optimal reactive power dispatch
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2015 · 2015
Later among the works it cites.
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine. 2016 · 2016
Later among the works it cites.
# Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel. 2016 · 2016
Later among the works it cites.
Lenient Learning in Independent-Learner Stochastic Cooperative Games
Ermo Wei and Sean Luke. 2016 · 2016
Later among the works it cites.
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
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Yinliang Xu, Wei Zhang, Wenxin Liu, and Frank Ferrese. 2012 · 2012
Cited alongside, same era.
A robust approach for multi-agent natural resource allocation based on stochastic optimization algorithms
Nikos Barbalios and Panagiotis Tzionas. 2014 · 2014
Cited alongside, same era.
Adam: A Method for Stochastic Optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR)
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Ioannis Antonoglou, John Quan Tom Schaul, and David Silver. 2015 · 2015
Cited alongside, same era.
Evolutionary dynamics of multi-agent learning: A survey
Daan Bloembergen, Daniel Hennes, Michael Kaisers, and Karl Tuyls. 2015 · 2015
Cited alongside, same era.
The importance of experience replay database composition in deep reinforcement learning. In Deep Reinforcement Learning Workshop, NIPS
Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška. 2015 · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Philip Torr, Pushmeet Kohli, Shimon Whiteson, et al · 2017
Closest in time.
Cooperative Multi-Agent Control Using Deep Reinforcement Learning. In Proceedings of the Adaptive and Learning Agents workshop (at AAMAS 2017)
Jayesh K Gupta, Maxim Egorov, and Mykel Kochenderfer. 2017 · 2017
Closest in time.
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag, and Enrique Munoz de Cote. 2017 · 2017
Closest in time.
Playing FPS Games with Deep Reinforcement Learning
Guillaume Lample and Devendra Singh Chaplot. 2017 · 2017
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Deep decentralized multi-task multi-agent reinforcement learning under partial observability. In International Conference on Machine Learning
Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P How, and John Vian. 2017 · 2017
Closest in time.
Value-Decomposition Networks For Cooperative Multi-Agent Learning
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, and Thore Graepel. 2017 · 2017
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Multiagent cooperation and competition with deep reinforcement learning
Ardi Tampuu, Tambet Matiisen, Dorian Kodelja, Ilya Kuzovkin, Kristjan Korjus, Juhan Aru, Jaan Aru, and Raul Vicente. 2017 · 2017
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