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Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-action pairs.
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Keep doing what worked: Behavioral modelling priors for offline reinforcement learning
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An off-policy trust region policy optimization method with monotonic improvement guarantee for deep reinforcement learning
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James Queeney, Yannis Paschalidis, and Christos G Cassandras · 2021
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Adversarially trained actor critic for offline reinforcement learning
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Rorl: Robust offline reinforcement learning via conservative smoothing
Rui Yang, Chenjia Bai, Xiaoteng Ma, Zhaoran Wang, Chongjie Zhang, and Lei Han · 2022
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