adaptable policy
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Offline Model-based Adaptable Policy Learning Xiong-Hui Chen 1, Y ang Y u
In reinforcement learning, a promising direction to avoid online trial-and-error costs is learning from an offline dataset. Current offline reinforcement learning methods commonly learn in the policy space constrained to in-support regions by the offline dataset, in order to ensure the robustness of the outcome policies.
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