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Reinforcement learning algorithms can train agents that solve problems in complex, interesting environments.
Multi-agent reinforcement learning: Independent vs. cooperative agents
Ming Tan · 1993
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Markov games as a framework for multi-agent reinforcement learning
Michael L Littman · 1994
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Evolving virtual creatures
Karl Sims · 1994
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Temporal difference learning and td-gammon
Gerald Tesauro · 1995
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Multitask learning
Rich Caruana · 1998
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Competitive coevolution through evolutionary complexification
Kenneth O Stanley and Risto Miikkulainen · 2004
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Cooperative multi-agent learning: The state of the art
Liviu Panait and Sean Luke · 2005
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A comprehensive survey of multiagent reinforcement learning
Lucian Busoniu, Robert Babuska, and Bart De Schutter · 2008
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Character animation in two-player adversarial games
Kevin Wampler, Erik Andersen, Evan Herbst, Yongjoon Lee, and Zoran Popović · 2010
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Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems
Laetitia Matignon, Guillaume J Laurent, and Nadine Le Fort-Piat · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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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 · 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 Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 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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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 2016
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Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
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Emergence of locomotion behaviours in rich environments
Nicolas Heess, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, Ali Eslami, Martin Riedmiller, et al · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
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Opponent modeling in deep reinforcement learning
He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daumé III · 2016
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Deep reinforcement learning from self-play in imperfect-information games
Johannes Heinrich and David Silver · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Learning with opponent-learning awareness
Jakob Foerster, Richard Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, and Igor Mordatch
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Counterfactual multi-agent policy gradients
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson
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Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
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OpenAI Dota 2 1v1 bot, 2017
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Proximal policy optimization algorithms
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Intrinsic motivation and automatic curricula via asymmetric self-play
Sainbayar Sukhbaatar, Ilya Kostrikov, Arthur Szlam, and Rob Fergus · 2017
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Multiagent cooperation and competition with deep reinforcement learning
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