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Recent Multi-Agent Reinforcement Learning (MARL) literature has been largely focused on Centralized Training with Decentralized Execution (CTDE) paradigm.
Asynchronous methods for deep reinforcement learning. In International conference on machine learning . 1928–1937
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Modeling adaptive autonomous agents
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Multi-agent reinforcement learning: Independent vs. cooperative agents. In Proceedings of the tenth international conference on machine learning . 330–337
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Markov games as a framework for multi-agent reinforcement learning
Michael L Littman. 1994 · 1994
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Long short-term memory
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Eligibility traces for off-policy policy evaluation
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Optimal payoff functions for members of collectives
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Language emergence: Implications for applied linguistics—Introduction to the special issue
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Cooperative adaptive cruise control: A reinforcement learning approach
Charles Desjardins and Brahim Chaib-Draa. 2011 · 2011
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The world of independent learners is not Markovian
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Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model
Mohammad Gheshlaghi Azar, Rémi Munos, and Hilbert J Kappen. 2013 · 2013
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013 · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2015 · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Proceedings of the 30th International Conference on Neural Information Processing Systems . 2180–2188
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016 · 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 · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2016 · 2016
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Multi-agent reinforcement learning as a rehearsal for decentralized planning
Landon Kraemer and Bikramjit Banerjee. 2016 · 2016
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Multi-agent cooperation and the emergence of (natural) language
Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni. 2016 · 2016
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Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Christopher J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. 2016 · 2016
Cited alongside, same era.
Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Rob Fergus, et al · 2016
Cited alongside, same era.
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba. 2017 · 2017
Cited alongside, same era.
Stabilising experience replay for deep multi-agent reinforcement learning. In International conference on machine learning . PMLR, 1146–1155
Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip HS Torr, Pushmeet Kohli, and Shimon Whiteson. 2017 · 2017
Cited alongside, same era.
When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine. 2019 · 2019
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Model-based reinforcement learning for atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, et al · 2019
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Generating diverse high-fidelity images with vq-vae-2. In Advances in neural information processing systems . 14866–14876
Ali Razavi, Aaron van den Oord, and Oriol Vinyals. 2019 · 2019
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The StarCraft Multi-Agent Challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philiph H. S. Torr, Jakob Foerster, and Shimon Whiteson. 2019 · 2019
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Cooperative multi-agent control using deep reinforcement learning. In International Conference on Autonomous Agents and Multiagent Systems . Springer, 66–83
Jayesh K Gupta, Maxim Egorov, and Mykel Kochenderfer. 2017 · 2017
Cited alongside, same era.
Emergence of language with multi-agent games: Learning to communicate with sequences of symbols
Serhii Havrylov and Ivan Titov. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch. 2017 · 2017
Cited alongside, same era.
Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu. 2017 · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning. In International Conference on Machine Learning . PMLR, 5887–5896
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi. 2019 · 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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Model-based reinforcement learning with a generative model is minimax optimal. In Conference on Learning Theory . PMLR, 67–83
Alekh Agarwal, Sham Kakade, and Lin F Yang. 2020 · 2020
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The emergence of adversarial communication in multi-agent reinforcement learning
Jan Blumenkamp and Amanda Prorok. 2020 · 2020
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Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
Christian Schroeder de Witt, Tarun Gupta, Denys Makoviichuk, Viktor Makoviychuk, Philip HS Torr, Mingfei Sun, and Shimon Whiteson. 2020 · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Mastering atari with discrete world models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba. 2020 · 2020
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Multi-agent reinforcement learning with multi-step generative models. In Conference on Robot Learning . PMLR, 776–790
Orr Krupnik, Igor Mordatch, and Aviv Tamar. 2020 · 2020
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Breaking the sample size barrier in model-based reinforcement learning with a generative model
Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, and Yuxin Chen. 2020 · 2020
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Multi-agent game abstraction via graph attention neural network. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 7211–7218
Yong Liu, Weixun Wang, Yujing Hu, Jianye Hao, Xingguo Chen, and Yang Gao. 2020 · 2020
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Flatland-RL: Multi-agent reinforcement learning on trains
Sharada Mohanty, Erik Nygren, Florian Laurent, Manuel Schneider, Christian Scheller, Nilabha Bhattacharya, Jeremy Watson, Adrian Egli, Christian Eichenberger, Christian Baumberger, et al · 2020
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
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Off-policy multi-agent decomposed policy gradients
Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, and Chongjie Zhang. 2020a · 2020
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Model-based multi-agent rl in zero-sum markov games with near-optimal sample complexity
Kaiqing Zhang, Sham M Kakade, Tamer Başar, and Lin F Yang. 2020 · 2020
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Drone Deep Reinforcement Learning: A Review
Ahmad Taher Azar, Anis Koubaa, Nada Ali Mohamed, Habiba A Ibrahim, Zahra Fathy Ibrahim, Muhammad Kazim, Adel Ammar, Bilel Benjdira, Alaa M Khamis, Ibrahim A Hameed, et al · 2021
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Multi-agent deep reinforcement learning: a survey
Sven Gronauer and Klaus Diepold. 2021 · 2021
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HPC Resources of the Higher School of Economics. In Journal of Physics: Conference Series , Vol. 1740. IOP Publishing, 012050
PS Kostenetskiy, RA Chulkevich, and VI Kozyrev. 2021 · 2021
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Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
Florian Laurent, Manuel Schneider, Christian Scheller, Jeremy Watson, Jiaoyang Li, Zhe Chen, Yi Zheng, Shao-Hung Chan, Konstantin Makhnev, Oleg Svidchenko, et al · 2021
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A sharp analysis of model-based reinforcement learning with self-play. In International Conference on Machine Learning . PMLR, 7001–7010
Qinghua Liu, Tiancheng Yu, Yu Bai, and Chi Jin. 2021 · 2021
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Emergent Communication under Competition
Michael Noukhovitch, Travis LaCroix, Angeliki Lazaridou, and Aaron Courville. 2021 · 2021
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Deep latent competition: Learning to race using visual control policies in latent space
Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, and Daniela Rus. 2021 · 2021
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The surprising effectiveness of mappo in cooperative, multi-agent games
Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, and Yi Wu. 2021 · 2021
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