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Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and has made progress in various fields.
Game theory
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Multi-agent reinforcement learning: Independent vs. cooperative agents
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Coordination in evolutionary multi-agent-robotic system using fuzzy and genetic algorithm
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Td-gammon, a self-teaching backgammon program, achieves master-level play
Gerald Tesauro · 1994
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Temporal difference learning and td-gammon
Gerald Tesauro · 1995
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Linear least-squares algorithms for temporal difference learning
Steven J Bradtke and Andrew G Barto · 1996
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On cooperation in multi-agent systems
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The dynamics of reinforcement learning in cooperative multiagent systems
Caroline Claus and Craig Boutilier · 1998
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Integrating skills into multi-agent systems
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Actor-critic algorithms
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Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Gerhard Weiss · 1999
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An algorithm for distributed reinforcement learning in cooperative multi-agent systems
Martin Lauer and Martin Riedmiller · 2000
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Hierarchical multi-agent reinforcement learning
Rajbala Makar, Sridhar Mahadevan, and Mohammad Ghavamzadeh · 2001
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The complexity of decentralized control of markov decision processes
Daniel S Bernstein, Robert Givan, Neil Immerman, and Shlomo Zilberstein · 2002
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Coordinated reinforcement learning
Carlos Guestrin, Michail G. Lagoudakis, and Ronald Parr · 2002
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Information-based multi-agent exploration
M Baglietto, M Paolucci, L Scardovi, and R Zoppoli · 2002
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Context-specific multiagent coordination and planning with factored mdps
Carlos Guestrin, Shobha Venkataraman, and Daphne Koller · 2002
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Multi-agent reinforcement learning: a critical survey
Yoav Shoham, Rob Powers, and Trond Grenager · 2003
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Multi-agent coordination: Theory and applications
Jianghai Hu · 2003
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Cooperative multi-agent learning: The state of the art
Liviu Panait and Sean Luke · 2005
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An introduction to copulas
Roger B Nelsen · 2006
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Collaborative multiagent reinforcement learning by payoff propagation
Jelle R Kok and Nikos Vlassis · 2006
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Artificial General Intelligence
Ben Goertzel and Cassio Pennachin · 2007
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If multi-agent learning is the answer, what is the question?
Yoav Shoham, Rob Powers, and Trond Grenager · 2007
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A comprehensive survey of multiagent reinforcement learning
Lucian Busoniu, Robert Babuska, and Bart De Schutter · 2008
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Traffic simulation with sumo–simulation of urban mobility
Daniel Krajzewicz · 2010
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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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Scaling multi-agent learning in complex environments
Chongjie Zhang · 2011
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Coordinating decentralized learning and conflict resolution across agent boundaries
Shanjun Cheng · 2012
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Designing for Learning in An Open World
Gráinne Conole · 2012
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Leading ad hoc agents in joint action settings with multiple teammates
Noa Agmon and Peter Stone · 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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Game theory
Guillermo Owen · 2013
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Coordinating multi-agent reinforcement learning with limited communication
Chongjie Zhang and Victor Lesser · 2013
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A survey of multi-objective sequential decision-making
Diederik M Roijers, Peter Vamplew, Shimon Whiteson, and Richard Dazeley · 2013
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin A. Riedmiller · 2014
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Multiobjective reinforcement learning: A comprehensive overview
Chunming Liu, Xin Xu, and Dewen Hu · 2014
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Communicating with unknown teammates
Samuel Barrett, Noa Agmon, Noam Hazon, Sarit Kraus, and Peter Stone · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Deep recurrent q-learning for partially observable mdps
Matthew Hausknecht and Peter Stone · 2015
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Subset selection by pareto optimization
Chao Qian, Yang Yu, and Zhi-Hua Zhou · 2015
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Evolutionary dynamics of multi-agent learning: A survey
Daan Bloembergen, Karl Tuyls, Daniel Hennes, and Michael Kaisers · 2015
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A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 2015
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Fictitious self-play in extensive-form games
Johannes Heinrich, Marc Lanctot, and David Silver · 2015
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, and Nando de Freitas · 2016
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Deep reinforcement learning with double q-learning
Hado van Hasselt, Arthur Guez, and David Silver · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Learning to communicate with deep multi-agent reinforcement learning
Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, and Shimon Whiteson · 2016
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Decentralized multi-agent exploration with online-learning of gaussian processes
Alberto Viseras, Thomas Wiedemann, Christoph Manss, Lukas Magel, Joachim Mueller, Dmitriy Shutin, and Luis Merino · 2016
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Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Arthur Szlam, and Rob Fergus · 2016
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Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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Individual planning in open and typed agent systems
Muthukumaran Chandrasekaran, A. Eck, Prashant Doshi, and Leen-Kiat Soh · 2016
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Multi-objective optimization
Kalyanmoy Deb, Karthik Sindhya, and Jussi Hakanen · 2016
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Deep reinforcement learning: An overview
Yuxi Li · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
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A distributional perspective on reinforcement learning
Marc G. Bellemare, Will Dabney, and Rémi Munos · 2017
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Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, and Koray Kavukcuoglu · 2017
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Neural episodic control
Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, and Charles Blundell · 2017
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Sample efficient actor-critic with experience replay
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, and Nando de Freitas · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Moo-mdp: An object-oriented representation for cooperative multiagent reinforcement learning
Felipe Leno Da Silva, Ruben Glatt, and Anna Helena Reali Costa · 2017
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Coordinated versus decentralized exploration in multi-agent multi-armed bandits
Mithun Chakraborty, Kai Yee Phoebe Chua, Sanmay Das, and Brendan Juba · 2017
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Coordinated multi-agent imitation learning
Hoang M Le, Yisong Yue, Peter Carr, and Patrick Lucey · 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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Open multi-agent systems: Gossiping with random arrivals and departures
Julien M Hendrickx and Samuel Martin · 2017
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Open decentralized pomdps
Jonathan Cohen, Jilles Steeve Dibangoye, and Abdel-Illah Mouaddib · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
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Reasoning about hypothetical agent behaviours and their parameters
Stefano V. Albrecht and Peter Stone · 2017
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Making friends on the fly: Cooperating with new teammates
Samuel Barrett, Avi Rosenfeld, Sarit Kraus, and Peter Stone · 2017
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Reinforcement Learning: An Introduction
Richard S Sutton and Andrew G Barto · 2018
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Multi-agent systems: A survey
Ali Dorri, Salil S Kanhere, and Raja Jurdak · 2018
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Value-decomposition networks for cooperative multi-agent learning based on team reward
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, and Thore Graepel · 2018
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Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
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Autonomous agents modelling other agents: A comprehensive survey and open problems
Stefano V Albrecht and Peter Stone · 2018
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Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al · 2018
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Counterfactual multi-agent policy gradients
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2018
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Machine theory of mind
Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, SM Ali Eslami, and Matthew Botvinick · 2018
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Multi-agent generative adversarial imitation learning
Jiaming Song, Hongyu Ren, Dorsa Sadigh, and Stefano Ermon · 2018
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Learning attentional communication for multi-agent cooperation
Jiechuan Jiang and Zongqing Lu · 2018
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Modeling others using oneself in multi-agent reinforcement learning
Roberta Raileanu, Emily Denton, Arthur Szlam, and Rob Fergus · 2018
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A deep policy inference q-network for multi-agent systems
Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, and Chun-Yi Lee · 2018
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Probabilistic recursive reasoning for multi-agent reinforcement learning
Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, and Wei Pan · 2018
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Generating multi-agent trajectories using programmatic weak supervision
Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, and Patrick Lucey · 2018
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Multi-agent imitation learning for driving simulation
Raunak P Bhattacharyya, Derek J Phillips, Blake Wulfe, Jeremy Morton, Alex Kuefler, and Mykel J Kochenderfer · 2018
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Competitive multi-agent inverse reinforcement learning with sub-optimal demonstrations
Xingyu Wang and Diego Klabjan · 2018
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Hierarchical deep multiagent reinforcement learning with temporal abstraction
Hongyao Tang, Jianye Hao, Tangjie Lv, Yingfeng Chen, Zongzhang Zhang, Hangtian Jia, Chunxu Ren, Yan Zheng, Zhaopeng Meng, Changjie Fan, et al · 2018
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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Magent: A many-agent reinforcement learning platform for artificial collective intelligence
Lianmin Zheng, Jiacheng Yang, Han Cai, Ming Zhou, Weinan Zhang, Jun Wang, and Yong Yu · 2018
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Pommerman: A multi-agent playground
Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob Foerster, Julian Togelius, Kyunghyun Cho, and Joan Bruna · 2018
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A survey and analysis of cooperative multi-agent robot systems: challenges and directions
Zool Hilmi Ismail, Nohaidda Sariff, and E Gorrostieta Hurtado · 2018
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Botzone: an online multi-agent competitive platform for ai education
Haoyu Zhou, Haifeng Zhang, Yushan Zhou, Xinchao Wang, and Wenxin Li · 2018
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Monte-carlo planning for team re-formation under uncertainty: Model and properties
Jonathan Cohen and Abdel-Illah Mouaddib · 2018
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, and Greg Wayne · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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Progress and prospects of the human–robot collaboration
Arash Ajoudani, Andrea Maria Zanchettin, Serena Ivaldi, Alin Albu-Schäffer, Kazuhiro Kosuge, and Oussama Khatib · 2018
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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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Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Dkebiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
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Dealing with non-stationarity in multi-agent deep reinforcement learning
Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, and Stefano V Albrecht · 2019
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Primal: Pathfinding via reinforcement and imitation multi-agent learning
Guillaume Sartoretti, Justin Kerr, Yunfei Shi, Glenn Wagner, TK Satish Kumar, Sven Koenig, and Howie Choset · 2019
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The Starcraft multi-agent challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schröder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob N. Foerster, and Shimon Whiteson · 2019
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A survey and critique of multiagent deep reinforcement learning
Pablo Hernandez-Leal, Bilal Kartal, and Matthew E Taylor · 2019
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A survey on transfer learning for multiagent reinforcement learning systems
Felipe Leno Da Silva and Anna Helena Reali Costa · 2019
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Actor-attention-critic for multi-agent reinforcement learning
Shariq Iqbal and Fei Sha · 2019
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MAVEN: multi-agent variational exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, and Shimon Whiteson · 2019
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Influence-based multi-agent exploration
Tonghan Wang, Jianhao Wang, Yi Wu, and Chongjie Zhang · 2019
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Efficient communication in multi-agent reinforcement learning via variance based control
Sai Qian Zhang, Qi Zhang, and Jieyu Lin · 2019
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Tarmac: Targeted multi-agent communication
Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, and Joelle Pineau · 2019
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Multi-agent adversarial inverse reinforcement learning
Lantao Yu, Jiaming Song, and Stefano Ermon · 2019
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Multi-agent interactions modeling with correlated policies
Minghuan Liu, Ming Zhou, Weinan Zhang, Yuzheng Zhuang, Jun Wang, Wulong Liu, and Yong Yu · 2019
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Feudal multi-agent hierarchies for cooperative reinforcement learning
S Ahilan and P Dayan · 2019
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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 · 2019
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Deep multi-agent reinforcement learning with discrete-continuous hybrid action spaces
Haotian Fu, Hongyao Tang, Jianye Hao, Zihan Lei, Yingfeng Chen, and Changjie Fan · 2019
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Coordinated exploration via intrinsic rewards for multi-agent reinforcement learning
Shariq Iqbal and Fei Sha · 2019
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Learning when to communicate at scale in multiagent cooperative and competitive tasks
Amanpreet Singh, Tushar Jain, and Sainbayar Sukhbaatar · 2019
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Theory of minds: Understanding behavior in groups through inverse planning
Michael Shum, Max Kleiman-Weiner, Michael L Littman, and Joshua B Tenenbaum · 2019
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Agent modeling as auxiliary task for deep reinforcement learning
Pablo Hernandez-Leal, Bilal Kartal, and Matthew E Taylor · 2019
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Modelling the dynamic joint policy of teammates with attention multi-agent ddpg
Hangyu Mao, Zhengchao Zhang, Zhen Xiao, and Zhibo Gong · 2019
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, , and Sergey Levine · 2019
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Multi-agent reinforcement learning with approximate model learning for competitive games
Young Joon Park, Yoon Sang Cho, and Seoung Bum Kim · 2019
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
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The representational capacity of action-value networks for multi-agent reinforcement learning
Jacopo Castellini, Frans A Oliehoek, Rahul Savani, and Shimon Whiteson · 2019
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The multi-agent reinforcement learning in malmö (marlö) competition
Diego Perez-Liebana, Katja Hofmann, Sharada Prasanna Mohanty, Noburu Kuno, Andre Kramer, Sam Devlin, Raluca D Gaina, and Daniel Ionita · 2019
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On the utility of learning about humans for human-ai coordination
Micah Carroll, Rohin Shah, Mark K. Ho, Tom Griffiths, Sanjit A. Seshia, Pieter Abbeel, and Anca D. Dragan · 2019
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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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Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario
Huichu Zhang, Siyuan Feng, Chang Liu, Yaoyao Ding, Yichen Zhu, Zihan Zhou, Weinan Zhang, Yong Yu, Haiming Jin, and Zhenhui Li · 2019
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Multi-agent pathfinding: Definitions, variants, and benchmarks
Roni Stern, Nathan R. Sturtevant, Ariel Felner, Sven Koenig, Hang Ma, Thayne T. Walker, Jiaoyang Li, Dor Atzmon, Liron Cohen, T. K. Satish Kumar, Roman Barták, and Eli Boyarski · 2019
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Maca: a multi-agent reinforcement learning platform for collective intelligence
Fang Gao, Si Chen, Mingqiang Li, and Bincheng Huang · 2019
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Hierarchical reinforcement learning for multi-agent moba game
Zhijian Zhang, Haozheng Li, Luo Zhang, Tianyin Zheng, Ting Zhang, Xiong Hao, Xiaoxin Chen, Min Chen, Fangxu Xiao, and Wei Zhou · 2019
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Diverse generation for multi-agent sports games
Raymond A Yeh, Alexander G Schwing, Jonathan Huang, and Kevin Murphy · 2019
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Virtual-taobao: Virtualizing real-world online retail environment for reinforcement learning
Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, and An-Xiang Zeng · 2019
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Learning with feature evolvable streams
Bo-Jian Hou, Lijun Zhang, and Zhi-Hua Zhou · 2019
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Open-ended learning in symmetric zero-sum games
David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki, Julien Perolat, Max Jaderberg, and Thore Graepel · 2019
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Learning to teach in cooperative multiagent reinforcement learning
Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, and Jonathan P. How · 2019
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Evolutionary population curriculum for scaling multi-agent reinforcement learning
Qian Long, Zihan Zhou, Abhinav Gupta, Fei Fang, Yi Wu, and Xiaolong Wang · 2019
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Power indices for team reformation planning under uncertainty
Jonathan Cohen and Abdel-Illah Mouaddib · 2019
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Stabilizing off-policy Q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
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Policy distillation and value matching in multiagent reinforcement learning
Samir Wadhwania, Dong-Ki Kim, Shayegan Omidshafiei, and Jonathan P. How · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Evolutionary learning: Advances in theories and algorithms
Zhi-Hua Zhou, Yang Yu, and Chao Qian · 2019
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α \alpha -rank: Multi-agent evaluation by evolution
Shayegan Omidshafiei, Christos Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M Czarnecki, Marc Lanctot, Julien Perolat, and Remi Munos · 2019
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Robust multi-agent reinforcement learning via minimax deep deterministic policy gradient
Shihui Li, Yi Wu, Xinyue Cui, Honghua Dong, Fei Fang, and Stuart Russell · 2019
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A generalized algorithm for multi-objective reinforcement learning and policy adaptation
Runzhe Yang, Xingyuan Sun, and Karthik Narasimhan · 2019
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Ad hoc teamwork with behavior switching agents
Manish Ravula, Shani Alkoby, and Peter Stone · 2019
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Learning existing social conventions via observationally augmented self-play
Adam Lerer and Alexander Peysakhovich · 2019
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Reinforcement learning applications
Yuxi Li · 2019
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Suphx: Mastering mahjong with deep reinforcement learning
Junjie Li, Sotetsu Koyamada, Qiwei Ye, Guoqing Liu, Chao Wang, Ruihan Yang, Li Zhao, Tao Qin, Tie-Yan Liu, and Hsiao-Wuen Hon · 2020
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An overview of multi-agent reinforcement learning from game theoretical perspective
Yaodong Yang and Jun Wang · 2020
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Enhanced poet: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions
Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, and Kenneth Stanley · 2020
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Sim-to-real transfer in deep reinforcement learning for robotics: a survey
Wenshuai Zhao, Jorge Peña Queralta, and Tomi Westerlund · 2020
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Robust multi-agent reinforcement learning with social empowerment for coordination and communication
Tessa van der Heiden, Christoph Salge, Efstratios Gavves, and Herke van Hoof · 2020
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Robust multi-agent reinforcement learning with model uncertainty
Kaiqing Zhang, Tao Sun, Yunzhe Tao, Sahika Genc, Sunil Mallya, and Tamer Basar · 2020
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Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications
Thanh Thi Nguyen, Ngoc Duy Nguyen, and Saeid Nahavandi · 2020
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Dop: Off-policy multi-agent decomposed policy gradients
Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, and Chongjie Zhang · 2020
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Learning individually inferred communication for multi-agent cooperation
Ziluo Ding, Tiejun Huang, and Zongqing Lu · 2020
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Variational autoencoders for opponent modeling in multi-agent systems
Georgios Papoudakis and Stefano V Albrecht · 2020
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Hierarchical cooperative multi-agent reinforcement learning with skill discovery
Jiachen Yang, Igor Borovikov, and Hongyuan Zha · 2020
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Rode: Learning roles to decompose multi-agent tasks
Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, and Chongjie Zhang · 2020
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Deep coordination graphs
Wendelin Boehmer, Vitaly Kurin, and Shimon Whiteson · 2020
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Agents teaching agents: a survey on inter-agent transfer learning
Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, and Peter Stone · 2020
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Learning agent communication under limited bandwidth by message pruning
Hangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong, and Yan Ni · 2020
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Learning multi-agent communication with double attentional deep reinforcement learning
Hangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong, and Yan Ni · 2020
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Learning nearly decomposable value functions via communication minimization
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