Abstract: Robust MDPs are proposed to handle the sensitive estimation errors in value estimation of MDPs, where the transition probability is allowed to take values in an uncertainty set. In recent years, many works have proposed computationally efficient learning algorithms to solve robust MDPs and obtained the near-optimal robust policy and value function. However, the statistical performances of the optimal robust policy and value function are less studied. In this talk, we will introduce the basic theories and algorithms of robust MDPs and figure out two questions: (a) How many samples are sufficient to guarantee the accuracy of the robust estimators; (b) whether it is possible to make statistical inferences from the robust estimators.