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We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces.
Self-improving reactive agents based on reinforcement learning, planning and teaching
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Modular robots
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A tutorial on the cross-entropy method
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Towards understanding linear value decomposition in cooperative multi-agent q-learning
Jianhao Wang, Zhizhou Ren, Beining Han, and Chongjie Zhang · 2006
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Optimal and approximate Q-value functions for decentralized pomdps
Frans A. Oliehoek, Matthijs T. J. Spaan, and Nikos Vlassis · 2008
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Qplex: Duplex dueling multi-agent q-learning
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Martin Riedmiller, Thomas Gabel, Roland Hafner, and Sascha Lange · 2009
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Mujoco: A physics engine for model-based control
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Design and architecture of the unified modular snake robot
Cornell Wright, Austin Buchan, Ben Brown, Jason Geist, Michael Schwerin, David Rollinson, Matthew Tesch, and Howie Choset · 2012
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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 Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Continuous deep q-learning with model-based acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, and Sergey Levine · 2016
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David Ha, Andrew Dai, and Quoc V Le · 2016
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Categorical reparameterization with gumbel-softmax
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Landon Kraemer and Bikramjit Banerjee · 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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Chainform: A linear integrated modular hardware system for shape changing interfaces
Ken Nakagaki, Artem Dementyev, Sean Follmer, Joseph A Paradiso, and Hiroshi Ishii · 2016
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NerveNet: learning structured policy with graph neural networks
Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler · 2018
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Reducing overestimation bias in multi-agent domains using double centralized critics
Johannes Ackermann, Volker Gabler, Takayuki Osa, and Masashi Sugiyama · 2019
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Factorised critics in deep multi-agent reinforcement learning
Marilena Bescuca · 2019
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LIIR: Learning individual intrinsic reward in multi-agent reinforcement learning
Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, and Dacheng Tao · 2019
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Actor-attention-critic for multi-agent reinforcement learning
Shariq Iqbal and Fei Sha · 2019
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A concise introduction to decentralized POMDPs , volume 1
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Input convex neural networks
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Cooperative multi-agent control using deep reinforcement learning
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Multi-agent actor-critic for mixed cooperative-competitive environments
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openai/baselines, May 2020
OpenAI · 2017
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Counterfactual multi-agent policy gradients
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Maven: Multi-agent variational exploration
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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
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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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Qtran++: Improved value transformation for cooperative multi-agent reinforcement learning
Kyunghwan Son, Sungsoo Ahn, Roben Delos Reyes, Jinwoo Shin, and Yung Yi · 2020
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Qatten: A general framework for cooperative multiagent reinforcement learning
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Contrasting centralized and decentralized critics in multi-agent reinforcement learning
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Dop: Off-policy multi-agent decomposed policy gradients
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