Linhai Qiu

Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning

While reinforcement learning has witnessed tremendous success recently in a wide range of domains, robustness–or the lack thereof–remains an important issue that remains inadequately addressed. In this paper, we provide a distributionally robust formulation of offline learning policy in tabular RL t…