Web: http://arxiv.org/abs/2209.06620

Sept. 15, 2022, 1:12 a.m. | Xiaoteng Ma, Zhipeng Liang, Li Xia, Jiheng Zhang, Jose Blanchet, Mingwen Liu, Qianchuan Zhao, Zhengyuan Zhou

stat.ML updates on arXiv.org arxiv.org

Among the reasons that hinder the application of reinforcement learning (RL)
to real-world problems, two factors are critical: limited data and the mismatch
of the testing environment compared to training one. In this paper, we attempt
to address these issues simultaneously with the problem setup of
distributionally robust offline RL. Particularly, we learn an RL agent with the
historical data obtained from the source environment and optimize it to perform
well in the perturbed one. Moreover, we consider the linear …

approximation arxiv function linear offline reinforcement reinforcement learning

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