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Proximal policy optimization (PPO) is one of the most successful deep reinforcement-learning methods, achieving state-of-the-art performance across a wide range of challenging tasks.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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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 · 1999
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A natural policy gradient
Sham M Kakade · 2001
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Approximately optimal approximate reinforcement learning
Sham Kakade and John Langford · 2002
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Reinforcement learning of motor skills with policy gradients
Jan Peters and Stefan Schaal · 2008
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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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
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Cited alongside, same era.
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
Cited alongside, same era.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, and Pieter Abbeel · 2016
Cited alongside, same era.
Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2017
Cited alongside, same era.
Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation
Yuhuai Wu, Elman Mansimov, Roger B Grosse, Shun Liao, and Jimmy Ba · 2017
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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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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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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Are deep policy gradient algorithms truly policy gradient algorithms?
Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 2018
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Riashat Islam, Peter Henderson, Maziar Gomrokchi, and Doina Precup · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2017
Cited alongside, same era.
Truly proximal policy optimization
Yuhui Wang, Hao He, and Xiaoyang Tan
Cited in the paper.
Trust region-guided proximal policy optimization
Yuhui Wang, Hao He, Xiaoyang Tan, and Yaozhong Gan
Cited in the paper.
Adaptive reinforcement learning control based on neural approximation for nonlinear discrete-time systems with unknown nonaffine dead-zone input
Yan-Jun Liu, Shu Li, Shaocheng Tong, and CL Philip Chen · 2018
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Reinforcement learning tracking control for robotic manipulator with kernel-based dynamic model
Yazhou Hu, Wenxue Wang, Hao Liu, and Lianqing Liu · 2019
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Using simulation to improve sample-efficiency of bayesian optimization for bipedal robots
Akshara Rai, Rika Antonova, Franziska Meier, and Christopher G. Atkeson · 2019
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