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Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is an important paradigm of RL.
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Double Q-learning
Hado Hasselt · 2010
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Sascha Lange, Thomas Gabel, and Martin Riedmiller · 2012
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Adam: A method for stochastic optimization
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Continuous control with deep reinforcement learning
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Deep unsupervised learning using nonequilibrium thermodynamics
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
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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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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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Stabilizing off-policy Q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 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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Score-based generative modeling through stochastic differential equations
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Is conditional generative modeling all you need for decision-making?
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Know your boundaries: The necessity of explicit behavioral cloning in offline rl
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