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Recent advances in multi-agent reinforcement learning (MARL) have achieved super-human performance in games like Quake 3 and Dota 2.
Temporal credit assignment in reinforcement learning
Richard S Sutton · 1985
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Multi-agent reinforcement learning: Independent vs. cooperative agents
Ming Tan · 1993
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Markov games as a framework for multi-agent reinforcement learning
Michael L Littman · 1994
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Coordination and adaptation in impromptu teams
Michael Bowling and Peter McCracken · 2005
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A comprehensive survey of multiagent reinforcement learning
Lucian Bu, Robert Babu, Bart De Schutter, et al · 2008
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Ad hoc autonomous agent teams: Collaboration without pre-coordination
Peter Stone, Gal A Kaminka, Sarit Kraus, and Jeffrey S Rosenschein · 2010
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Empirical evaluation of ad hoc teamwork in the pursuit domain
Samuel Barrett, Peter Stone, and Sarit Kraus · 2011
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Learning teammate models for ad hoc teamwork
Samuel Barrett, Peter Stone, Sarit Kraus, and Avi Rosenfeld · 2012
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Cooperating with a markovian ad hoc teammate
Doran Chakraborty and Peter Stone · 2013
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Cooperating with unknown teammates in complex domains: A robot soccer case study of ad hoc teamwork
Samuel Barrett and Peter Stone · 2015
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Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson · 2016
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Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Rob Fergus, et al · 2016
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Half field offense: An environment for multiagent learning and ad hoc teamwork
Matthew Hausknecht, Prannoy Mupparaju, Sandeep Subramanian, Shivaram Kalyanakrishnan, and Peter Stone · 2016
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Opponent modeling in deep reinforcement learning
He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daumé III · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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A unified game-theoretic approach to multiagent reinforcement learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, and Thore Graepel · 2017
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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, et al · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
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Multi-agent reinforcement learning in sequential social dilemmas
Joel Z Leibo, Vinicius Zambaldi, Marc Lanctot, Janusz Marecki, and Thore Graepel · 2017
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Continuous adaptation via meta-learning in nonstationary and competitive environments
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, and Pieter Abbeel · 2017
Emergent tool use from multi-agent autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, and Igor Mordatch · 2019
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Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi · 2019
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Efficient bimanual manipulation using learned task schemas
Rohan Chitnis, Shubham Tulsiani, Saurabh Gupta, and Abhinav Gupta · 2019
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Learning to interactively learn and assist
Mark Woodward, Chelsea Finn, and Karol Hausman · 2019
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Robust multi-agent reinforcement learning via minimax deep deterministic policy gradient
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Zero shot transfer learning for robot soccer
Devin Schwab, Yifeng Zhu, and Manuela Veloso · 2018
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Qmix: monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder De Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
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Counterfactual multi-agent policy gradients
Jakob N Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2018
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Actor-attention-critic for multi-agent reinforcement learning
Shariq Iqbal and Fei Sha · 2018
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Emergence of grounded compositional language in multi-agent populations
Igor Mordatch and Pieter Abbeel · 2018
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Benchmarks for reinforcement learning in mixed-autonomy traffic
Eugene Vinitsky, Aboudy Kreidieh, Luc Le Flem, Nishant Kheterpal, Kathy Jang, Cathy Wu, Fangyu Wu, Richard Liaw, Eric Liang, and Alexandre M Bayen · 2018
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Credit assignment for collective multiagent rl with global rewards
Duc Thien Nguyen, Akshat Kumar, and Hoong Chuin Lau · 2018
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Shihui Li, Yi Wu, Xinyue Cui, Honghua Dong, Fei Fang, and Stuart Russell · 2019
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Macheng Shen and Jonathan P How · 2019
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Finding friend and foe in multi-agent games
Jack Serrino, Max Kleiman-Weiner, David C Parkes, and Josh Tenenbaum · 2019
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Superhuman ai for multiplayer poker
Noam Brown and Tuomas Sandholm · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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M 3 RL: Mind-aware multi-agent management reinforcement learning
Tianmin Shu and Yuandong Tian · 2019
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A structured prediction approach for generalization in cooperative multi-agent reinforcement learning
Nicolas Carion, Nicolas Usunier, Gabriel Synnaeve, and Alessandro Lazaric · 2019
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" other-play" for zero-shot coordination
Hengyuan Hu, Adam Lerer, Alex Peysakhovich, and Jakob Foerster · 2020
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Evolutionary population curriculum for scaling multi-agent reinforcement learning
Qian Long, Zihan Zhou, Abhibav Gupta, Fei Fang, Yi Wu, and Xiaolong Wang · 2020
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Generating and adapting to diverse ad-hoc cooperation agents in hanab
Rodrigo Canaan, Xianbo Gao, Julian Togelius, Andy Nealen, and Stefan Menzel · 2020
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Cm3: Cooperative multi-goal multi-stage multi-agent reinforcement learning
Jiachen Yang, Alireza Nakhaei, David Isele, Kikuo Fujimura, and Hongyuan Zha · 2020
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