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In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines.
Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, George Tucker, and Sergey Levine · 1906
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Introduction to Reinforcement Learning
Richard S. Sutton and Andrew G. Barto · 1998
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Nicolas Meuleau, Leonid Peshkin, Leslie P. Kaelbling, and Kee eung Kim · 2000
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour · 2000
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Off-policy temporal difference learning with function approximation
Doina Precup, Richard S. Sutton, and Sanjoy Dasgupta · 2001
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Sham Kakade and John Langford · 2002
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Reinforcement learning by reward-weighted regression for operational space control
Jan Peters and Stefan Schaal · 2007
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Adaptive importance sampling for value function approximation in off-policy reinforcement learning
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiayma, and Jan Peters · 2009
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Policy search for motor primitives in robotics
Jens Kober and Jan R. Peters · 2009
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Fitted q-iteration by advantage weighted regression
Gerhard Neumann and Jan R. Peters · 2009
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 2010
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Jan Peters, Katharina Mülling, and Yasemin Altün · 2010
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Gerhard Neumann · 2011
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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
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John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
Emergence of locomotion behaviours in rich environments
Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin A. Riedmiller, and David Silver · 2017
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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Daniel Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, and David Silver · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Maximum a posteriori policy optimisation
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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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Deep reinforcement learning with double q-learning
Hado van Hasselt, Arthur Guez, and David Silver · 2016
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Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Manfred Otto Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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Safe and efficient off-policy reinforcement learning
Rémi Munos, Thomas Stepleton, Anna Harutyunyan, and Marc G. Bellemare · 2016
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Sample efficient actor-critic with experience replay
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, and Nando de Freitas · 2016
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Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov · 2017
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Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, and Martin Riedmiller · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
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Learning gaussian policies from smoothed action value functions, 2018
Ofir Nachum, Mohammad Norouzi, George Tucker, and Dale Schuurmans · 2018
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Deepmimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne · 2018
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Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2018
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Mode-adaptive neural networks for quadruped motion control
He Zhang, Sebastian Starke, Taku Komura, and Jun Saito · 2018
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Diagnosing bottlenecks in deep q-learning algorithms
Justin Fu, Aviral Kumar, Matthew Soh, and Sergey Levine · 2019
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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