Robust analysis of discounted Markov decision processes with uncertain transition probabilities

Optimal policies in Markov decision problems may be quite sensitive with regard to transition probabilities. In practice, some transition probabilities may be uncertain. The goals of the present study are to find the robust range for a certain optimal policy and to obtain value intervals of exact transition probabilities. Our research yields powerful contributions for Markov decision processes (MDPs) with uncertain transition probabilities. We first propose a method for estimating unknown transition probabilities based on maximum likelihood. Since the estimation may be far from accurate, and the highest expected total reward of the MDP may be sensitive to these transition probabilities, we analyze the robustness of an optimal policy and propose an approach for robust analysis. After giving the definition of a robust optimal policy with uncertain transition probabilities represented as sets of numbers, we formulate a model to obtain the optimal policy. Finally, we define the value intervals of the exact transition probabilities and construct models to determine the lower and upper bounds. Numerical examples are given to show the practicability of our methods.