Fetching the paper…
Reading the bibliography…
Cooperative multi-agent reinforcement learning (MARL) has achieved significant results, most notably by leveraging the representation-learning abilities of deep neural networks.
Multi-agent reinforcement learning: Independent vs. cooperative agents
Ming Tan · 1993
Earlier work this paper cites.
Temporal difference learning and td-gammon
Gerald Tesauro · 1995
Earlier work this paper cites.
Hierarchical reinforcement learning with the maxq value function decomposition
Thomas G Dietterich · 2000
Earlier work this paper cites.
A probabilistic approach to collaborative multi-robot localization
Dieter Fox, Wolfram Burgard, Hannes Kruppa, and Sebastian Thrun · 2000
Earlier work this paper cites.
Cooperative multi-agent learning: The state of the art
Liviu Panait and Sean Luke · 2005
Earlier work this paper cites.
Multi-agent framework for third party logistics in e-commerce
Wang Ying and Sang Dayong · 2005
Earlier work this paper cites.
If multi-agent learning is the answer, what is the question?
Yoav Shoham, Rob Powers, and Trond Grenager · 2007
Earlier work this paper cites.
A comprehensive survey of multiagent reinforcement learning
Lucian Busoniu, Robert Babuska, and Bart De Schutter · 2008
Earlier work this paper cites.
Multi-agent systems in a distributed smart grid: Design and implementation
Manisa Pipattanasomporn, Hassan Feroze, and Saifur Rahman · 2009
Earlier work this paper cites.
Communication, credibility and negotiation using a cognitive hierarchy model
Michael Wunder, Michael Littman, and Matthew Stone · 2009
Earlier work this paper cites.
Innovations in agent-based complex automated negotiations
Takayuki Ito, Minjie Zhang, Valentin Robu, Shaheen Fatima, Tokuro Matsuo, and Hirofumi Yamaki · 2010
Earlier work this paper cites.
Optimization and coordinated autonomy in mobile fulfillment systems
John J Enright and Peter R Wurman · 2011
Earlier work this paper cites.
Discovering binary codes for documents by learning deep generative models
Geoffrey Hinton and Ruslan Salakhutdinov · 2011
Earlier work this paper cites.
Accelerating best response calculation in large extensive games
Michael Johanson, Kevin Waugh, Michael Bowling, and Martin Zinkevich · 2011
Earlier work this paper cites.
The world of independent learners is not markovian
Guillaume J Laurent, Laëtitia Matignon, Le Fort-Piat, et al · 2011
Earlier work this paper cites.
Protecting against evaluation overfitting in empirical reinforcement learning
Shimon Whiteson, Brian Tanner, Matthew E Taylor, and Peter Stone · 2011
Earlier work this paper cites.
An overview of recent progress in the study of distributed multi-agent coordination
Yongcan Cao, Wenwu Yu, Wei Ren, and Guanrong Chen · 2012
Earlier work this paper cites.
Coordinated multi-robot exploration under communication constraints using decentralized markov decision processes
Laëtitia Matignon, Laurent Jeanpierre, and Abdel-Illah Mouaddib · 2012
Earlier work this paper cites.
Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems
Laetitia Matignon, Guillaume J Laurent, and Nadine Le Fort-Piat · 2012
Earlier work this paper cites.
Coevolving game-playing agents: Measuring performance and intransitivities
Spyridon Samothrakis, Simon Lucas, ThomasPhilip Runarsson, and David Robles · 2012
Earlier work this paper cites.
Improved memory-bounded dynamic programming for decentralized pomdps
Sven Seuken and Shlomo Zilberstein · 2012
Earlier work this paper cites.
Multiagent learning: Basics, challenges, and prospects
Karl Tuyls and Gerhard Weiss · 2012
Cited alongside, same era.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Reinforcement learning agents providing advice in complex video games
Matthew E. Taylor, Nicholas Carboni * · 2014
Cited alongside, same era.
Heads-up limit hold’em poker is solved
Michael Bowling, Neil Burch, Michael Johanson, and Oskari Tammelin · 2015
Cited alongside, same era.
Cooperative deep reinforcement learning for tra ic signal control
Mengqi Liu, Jiachuan Deng, Ming Xu, Xianbo Zhang, and Wei Wang · 2017
Later among the works it cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
Later among the works it cites.
Peng Peng, Ying Wen, Yaodong Yang, Quan Yuan, Zhenkun Tang, Haitao Long, and Jun Wang · 2017
Later among the works it cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Later among the works it cites.
Value-decomposition networks for cooperative multi-agent learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 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.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Cited alongside, same era.
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
Cited alongside, same era.
Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson · 2016
Cited alongside, same era.
Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, and Shimon Whiteson · 2016
Cited alongside, same era.
Multi-agent cooperation and the emergence of (natural) language
Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni · 2016
Cited alongside, same era.
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
Later among the works it cites.
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
Later among the works it cites.
Feudal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
Later among the works it cites.
Autonomous agents modelling other agents: A comprehensive survey and open problems
Stefano V Albrecht and Peter Stone · 2018
Later among the works it cites.
Malthusian reinforcement learning
Joel Z Leibo, Julien Perolat, Edward Hughes, Steven Wheelwright, Adam H Marblestone, Edgar Duéñez-Guzmán, Peter Sunehag, Iain Dunning, and Thore Graepel · 2018
Later among the works it cites.
Emergence of grounded compositional language in multi-agent populations
Igor Mordatch and Pieter Abbeel · 2018
Later among the works it cites.
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
Later among the works it cites.
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, et al · 2018
Later among the works it cites.
Hierarchical deep multiagent reinforcement learning
Hongyao Tang, Jianye Hao, Tangjie Lv, Yingfeng Chen, Zongzhang Zhang, Hangtian Jia, Chunxu Ren, Yan Zheng, Changjie Fan, and Li Wang · 2018
Later among the works it cites.
Message-dropout: An efficient training method for multi-agent deep reinforcement learning
Woojun Kim, Myungsik Cho, and Youngchul Sung · 2019
Later among the works it cites.
Joel Z Leibo, Edward Hughes, Marc Lanctot, and Thore Graepel · 2019
Later among the works it cites.
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
Later among the works it cites.
Learning to communicate in multi-agent reinforcement learning: A review
Mohamed Salah Zaïem and Etienne Bennequin · 2019
Later among the works it cites.
Learning structured communication for multi-agent reinforcement learning
Junjie Sheng, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenhao Li, Tsung-Hui Chang, Jun Wang, and Hongyuan Zha · 2020
Later among the works it cites.
Agents teaching agents: a survey on inter-agent transfer learning
Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, and Peter Stone · 2020
Later among the works it cites.
Smarts: Scalable multi-agent reinforcement learning training school for autonomous driving
Ming Zhou, Jun Luo, Julian Villela, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, et al · 2020
Later among the works it cites.