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Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards.
Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, George Tucker, and Sergey Levine · 1906
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Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, George Tucker, and Sergey Levine · 1906
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 1910
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Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
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Learning to achieve goals
Leslie Pack Kaelbling · 1993
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Analysis of temporal-diffference learning with function approximation
John N Tsitsiklis and Benjamin Van Roy · 1997
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Approximately optimal approximate reinforcement learning
Sham Kakade and John Langford · 2002
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The cross entropy method for fast policy search
Shie Mannor, Reuven Y Rubinstein, and Yohai Gat · 2003
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Reinforcement learning by reward-weighted regression for operational space control
Jan Peters and Stefan Schaal · 2007
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Robot programming by demonstration
Aude Billard, Sylvain Calinon, Ruediger Dillmann, and Stefan Schaal · 2008
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Fitted q-iteration by advantage weighted regression
Gerhard Neumann and Jan R Peters · 2009
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A generalized path integral control approach to reinforcement learning
Evangelos Theodorou, Jonas Buchli, and Stefan Schaal · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stephane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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The cross-entropy method optimizes for quantiles
Sergiu Goschin, Ari Weinstein, and Michael Littman · 2013
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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba · 2016
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Reward augmented maximum likelihood for neural structured prediction
Mohammad Norouzi, Samy Bengio, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans, et al · 2016
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Improving policy gradient by exploring under-appreciated rewards
Ofir Nachum, Mohammad Norouzi, and Dale Schuurmans · 2016
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Maximum a posteriori policy optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, and Martin Riedmiller · 2018
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Temporal difference models: Model-free deep rl for model-based control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal, and Sergey Levine · 2018
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Zero-shot visual imitation
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A Efros, and Trevor Darrell · 2018
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Semi-parametric topological memory for navigation
Nikolay Savinov, Alexey Dosovitskiy, and Vladlen Koltun · 2018
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Visual reinforcement learning with imagined goals
Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
Cited alongside, same era.
Paulo Rauber, Avinash Ummadisingu, Filipe Mutz, and Juergen Schmidhuber · 2017
Cited alongside, same era.
Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne · 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.
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin Riedmiller · 2017
Cited alongside, same era.
Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
Cited alongside, same era.
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2018
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Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 2019
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Goal conditioned imitation learning
Yiming Ding, Carlos Florensa, Mariano Phielipp, and Pieter Abbeel · 2019
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Independent generative adversarial self-imitation learning in cooperative multiagent systems
Xiaotian Hao, Weixun Wang, Jianye Hao, and Y. Yang · 2019
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
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Search on the replay buffer: Bridging planning and reinforcement learning
Benjamin Eysenbach, Ruslan Salakhutdinov, and Sergey Levine · 2019
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Learning actionable representations with goal conditioned policies
Dibya Ghosh, Abhishek Gupta, and Sergey Levine · 2019
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Robel: Robotics benchmarks for learning with low-cost robots, 2019
Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, and Vikash Kumar · 2019
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