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Trust region methods rigorously enabled reinforcement learning (RL) agents to learn monotonically improving policies, leading to superior performance on a variety of tasks.
Non-cooperative games
John Nash · 1951
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Michael L Littman · 1994
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Caroline Claus and Craig Boutilier · 1998
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R. S. Sutton, D. Mcallester, S. Singh, and Y. Mansour · 2000
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Sham Kakade and John Langford · 2002
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Christian Schröder de Witt, Bei Peng, Pierre-Alexandre Kamienny, Philip H. S. Torr, Wendelin Böhmer, and Shimon Whiteson · 2003
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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 · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
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Multiagent bidirectionally-coordinated nets for learning to play starcraft combat games. arxiv 2017
P Peng, Q Yuan, Y Wen, Y Yang, Z Tang, H Long, and J Wang · 2017
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Proximal policy optimization algorithms
John Schulman, F. Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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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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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Benchmarking reinforcement learning algorithms on real-world robots
A Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, and James Bergstra · 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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Value-decomposition networks for cooperative multi-agent learning based on team reward
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al · 2018
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Multi-agent trust region policy optimization
Hepeng Li and Haibo He · 2020
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Modelling bounded rationality in multi-agent interactions by generalized recursive reasoning
Ying Wen, Yaodong Yang, and Jun Wang · 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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Multi-agent determinantal q-learning
Yaodong Yang, Ying Wen, Jun Wang, Liheng Chen, Kun Shao, David Mguni, and Weinan Zhang · 2020
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Bi-level actor-critic for multi-agent coordination
Haifeng Zhang, Weizhe Chen, Zeren Huang, Minne Li, Yaodong Yang, Weinan Zhang, and Jun Wang · 2020
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 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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Mean field multi-agent reinforcement learning
Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang · 2018
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The starcraft multi-agent challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder De Witt, Gregory Farquhar, Nantas Nardelli, Tim GJ Rudner, Chia-Man Hung, Philip HS Torr, Jakob Foerster, and Shimon Whiteson · 2019
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Eigengame: Pca as a nash equilibrium
Ian Gemp, Brian McWilliams, Claire Vernade, and Thore Graepel · 2020
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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
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