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In offline reinforcement learning, it is necessary to manage out-of-distribution actions to prevent overestimation of value functions.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1999
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Adam: A method for stochastic optimization
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Off-policy deep reinforcement learning without exploration
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Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
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Diganta Misra · 2019
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Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, and Dale Schuurmans · 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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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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An optimistic perspective on offline reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
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Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Conservative q-learning for offline reinforcement learning
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Ofir Nachum and Bo Dai · 2020
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Offline reinforcement learning via high-fidelity generative behavior modeling
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Why so pessimistic? estimating uncertainties for offline rl through ensembles, and why their independence matters
Kamyar Ghasemipour, Shixiang Shane Gu, and Ofir Nachum · 2022
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Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua B Tenenbaum, and Sergey Levine · 2022
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Offline-to-online reinforcement learning via balanced replay and pessimistic q-ensemble
Seunghyun Lee, Younggyo Seo, Kimin Lee, Pieter Abbeel, and Jinwoo Shin · 2022
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Diffusion policies as an expressive policy class for offline reinforcement learning
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Score-based generative modeling through stochastic differential equations
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Uncertainty-based offline reinforcement learning with diversified q-ensemble
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Offline rl without off-policy evaluation
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Learning a diffusion model policy from rewards via q-score matching
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Offline rl with no ood actions: In-sample learning via implicit value regularization
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Policy representation via diffusion probability model for reinforcement learning
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Behavior proximal policy optimization
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Diffusion policies creating a trust region for offline reinforcement learning
Tianyu Chen, Zhendong Wang, and Mingyuan Zhou · 2024
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An implicit trust region approach to behavior regularized offline reinforcement learning
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