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In offline reinforcement learning, weighted regression is a common method to ensure the learned policy stays close to the behavior policy and to prevent selecting out-of-sample actions.
Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, George Tucker, and Sergey Levine · 1906
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A family of embedded runge-kutta formulae
John R Dormand and Peter J Prince · 1980
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Introduction to Reinforcement Learning
Richard S. Sutton and Andrew G. Barto · 1998
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
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Relative entropy policy search
Jan Peters, Katharina Mulling, and Yasemin Altun · 2010
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin A. Riedmiller · 2014
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Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Charles Blundell, Benigno Uria, Alexander Pritzel, Yazhe Li, Avraham Ruderman, Joel Z Leibo, Jack Rae, Daan Wierstra, and Demis Hassabis · 2016
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Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 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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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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Efficient parallel methods for deep reinforcement learning
Alfredo V Clemente, Humberto N Castejón, and Arjun Chandra · 2017
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Implicit generation and modeling with energy based models
Yilun Du and Igor Mordatch · 2019
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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James Lucas, George Tucker, Roger Grosse, and Mohammad Norouzi · 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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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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BAIL: Best-action imitation learning for batch deep reinforcement learning
Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, and Keith Ross · 2020
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Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Quinn Nichol · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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Emaq: Expected-max q-learning operator for simple yet effective offline and online rl
Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, and Shixiang Shane Gu · 2021
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Generalizable episodic memory for deep reinforcement learning
Hao Hu, Jianing Ye, Zhizhou Ren, Guangxiang Zhu, and Chongjie Zhang · 2021
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Offline reinforcement learning with fisher divergence critic regularization
Ilya Kostrikov, Rob Fergus, Jonathan Tompson, and Ofir Nachum · 2021
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Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Generative adversarial networks
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Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Conservative Q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Energy-based imitation learning
Minghuan Liu, Tairan He, Minkai Xu, and Weinan Zhang · 2020
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Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
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Parrot: Data-driven behavioral priors for reinforcement learning
Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, and Sergey Levine · 2020
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Wenxuan Zhou, Sujay Bajracharya, and David Held · 2021
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Lapo: Latent-variable advantage-weighted policy optimization for offline reinforcement learning
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Diffusion policies as an expressive policy class for offline reinforcement learning
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Tianshou: A highly modularized deep reinforcement learning library
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