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Offline reinforcement learning algorithms have proven effective on datasets highly connected to the target downstream task.
A Markovian decision process
Richard Bellman · 1957
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Weighted average importance sampling and defensive mixture distributions
Tim Hesterberg · 1995
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Reinforcement learning by reward-weighted regression for operational space control
Jan Peters and Stefan Schaal · 2007
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2015
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Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 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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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 2018
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2019
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
Cited alongside, same era.
Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
Cited alongside, same era.
Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
Cited alongside, same era.
What matters in on-policy reinforcement learning? a large-scale empirical study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphael Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, et al · 2020
Cited alongside, same era.
On warm-starting neural network training
Jordan T. Ash and Ryan P. Adams · 2020
Cited alongside, same era.
Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2022
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Aw-opt: Learning robotic skills with imitation and reinforcement at scale
Yao Lu, Karol Hausman, Yevgen Chebotar, Mengyuan Yan, Eric Jang, Alexander Herzog, Ted Xiao, Alex Irpan, Mohi Khansari, Dmitry Kalashnikov, et al · 2022
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The primacy bias in deep reinforcement learning
Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, and Aaron Courville · 2022
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Mocapact: A multi-task dataset for simulated humanoid control
Nolan Wagener, Andrey Kolobov, Felipe Vieira Frujeri, Ricky Loynd, Ching-An Cheng, and Matthew Hausknecht · 2022
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Don’t change the algorithm, change the data: Exploratory data for offline reinforcement learning
Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin, Pieter Abbeel, Alessandro Lazaric, and Lerrel Pinto · 2022
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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Rl unplugged: A suite of benchmarks for offline reinforcement learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez, Konrad Zolna, Rishabh Agarwal, Josh S Merel, Daniel J Mankowitz, Cosmin Paduraru, et al · 2020
Cited alongside, same era.
Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Maxmin q-learning: Controlling the estimation bias of q-learning
Qingfeng Lan, Yangchen Pan, Alona Fyshe, and Martha White · 2020
Cited alongside, same era.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
Cited alongside, same era.
Critic regularized regression
Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh S Merel, Jost Tobias Springenberg, Scott E Reed, Bobak Shahriari, Noah Siegel, Caglar Gulcehre, Nicolas Heess, and Nando de Freitas · 2020
Cited alongside, same era.
Learning pessimism for reinforcement learning
Edoardo Cetin and Oya Celiktutan · 2023
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Loss of plasticity in deep continual learning, 2023
Shibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman, Richard S. Sutton, and A. Rupam Mahmood · 2023
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Sample-efficient reinforcement learning by breaking the replay ratio barrier
Pierluca D’Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, and Aaron Courville · 2023
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For sale: State-action representation learning for deep reinforcement learning
Scott Fujimoto, Wei-Di Chang, Edward J Smith, Shixiang Shane Gu, Doina Precup, and David Meger · 2023
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Extreme q-learning: Maxent RL without entropy
Divyansh Garg, Joey Hejna, Matthieu Geist, and Stefano Ermon · 2023
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Understanding plasticity in neural networks, 2023
Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Avila Pires, Razvan Pascanu, and Will Dabney · 2023
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Learning on the job: Self-rewarding offline-to-online finetuning for industrial insertion of novel connectors from vision
Ashvin Nair, Brian Zhu, Gokul Narayanan, Eugen Solowjow, and Sergey Levine · 2023
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Bigger, better, faster: Human-level atari with human-level efficiency
Max Schwarzer, Johan Samir Obando Ceron, Aaron Courville, Marc G Bellemare, Rishabh Agarwal, and Pablo Samuel Castro · 2023
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Reds: Offline rl with heteroskedastic datasets via support constraints
Anikait Singh, Aviral Kumar, Quan Vuong, Yevgen Chebotar, and Sergey Levine · 2023
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Diffusion policies as an expressive policy class for offline reinforcement learning
Zhendong Wang, Jonathan J. Hunt, and Mingyuan Zhou · 2023
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Offline imitation learning with suboptimal demonstrations via relaxed distribution matching
Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, and Stefano Ermon · 2023
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Offline prioritized experience replay
Yang Yue, Bingyi Kang, Xiao Ma, Gao Huang, Shiji Song, and Shuicheng Yan · 2023
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