Web: http://arxiv.org/abs/2209.08025

Sept. 19, 2022, 1:12 a.m. | Mengdi Xu, Zuxin Liu, Peide Huang, Wenhao Ding, Zhepeng Cen, Bo Li, Ding Zhao

cs.LG updates on arXiv.org arxiv.org

A trustworthy reinforcement learning algorithm should be competent in solving
challenging real-world problems, including {robustly} handling uncertainties,
satisfying {safety} constraints to avoid catastrophic failures, and
{generalizing} to unseen scenarios during deployments. This study aims to
overview these main perspectives of trustworthy reinforcement learning
considering its intrinsic vulnerabilities on robustness, safety, and
generalizability. In particular, we give rigorous formulations, categorize
corresponding methodologies, and discuss benchmarks for each perspective.
Moreover, we provide an outlook section to spur promising future directions
with a …

arxiv reinforcement reinforcement learning robustness safety trustworthy vulnerabilities

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