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The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world.
Temporal difference learning and TD-Gammon
Gerald Tesauro · 1995
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y Ng, Daishi Harada, and Stuart Russell · 1999
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Eligibility traces for off-policy policy evaluation
Doina Precup, Richard S. Sutton, and Satinder P. Singh · 2000
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Autonomous helicopter flight via reinforcement learning
Andrew Y Ng, H Jin Kim, Michael I Jordan, Shankar Sastry, and Shiv Ballianda · 2003
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Agent Based Decision Support System Using Reinforcement Learning Under Emergency Circumstances , volume 3610
Devinder Thapa, In-Sung Jung, and Gi-Nam Wang · 2005
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Maximum entropy inverse reinforcement learning
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Doubly robust policy evaluation and learning
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Optimizing the next generation collision avoidance system for safe, suitable, and acceptable operational performance
Jessica E Holland, Mykel J Kochenderfer, and Wesley A Olson · 2013
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Playing Atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Off-policy evaluation in Markov decision processes
Cosmin Paduraru · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Doubly robust off-policy value evaluation for reinforcement learning
Nan Jiang and Lihong Li · 2016
Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine · 2018
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Rllib: Abstractions for distributed reinforcement learning
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica · 2018
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Scaling data-driven robotics with reward sketching and batch reinforcement learning
Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerik, et al · 2019
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Off-policy evaluation via off-policy classification
Alex Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, and Sergey Levine · 2019
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Deep reinforcement learning from human preferences
Paul Christiano, Jan Leike, Tom B Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Learning robust rewards with adversarial inverse reinforcement learning
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Proximal policy optimization algorithms
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Mastering the game of Go without human knowledge
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Hoang M. Le, Cameron Voloshin, and Yisong Yue · 2019
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Truly batch apprenticeship learning with deep successor features
Donghun Lee, Srivatsan Srinivasan, and Finale Doshi-Velez · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Quantifying differences in reward functions
Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, and Jan Leike · 2020
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Reward (mis)design for autonomous driving
W Bradley Knox, Alessandro Allievi, Holger Banzhaf, Felix Schmitt, and Peter Stone · 2021
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