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Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence.
Kenny Young and Tian Tian · 1903
Earlier work this paper cites.
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Steven Dalton, Iuri Frosio, and Michael Garland · 1907
Earlier work this paper cites.
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Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, and Hado Van Hasselt · 1908
Earlier work this paper cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 1910
Earlier work this paper cites.
Markov decision processes
Martin L Puterman · 1990
Earlier work this paper cites.
Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra · 1998
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S. Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
Earlier work this paper cites.
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Earlier work this paper cites.
Multiplayer support for the arcade learning environment, 2021b
J. K. Terry, Benjamin Black, and Luis Santos · 2009
Earlier work this paper cites.
Charles Beattie, Thomas Köppe, Edgar A. Duéñez-Guzmán, and Joel Z. Leibo · 2011
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
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, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
ViZDoom: A Doom-based AI research platform for visual reinforcement learning
Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski · 2016
Earlier work this paper cites.
Openai baselines
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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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 · 2018
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