Robustness in Markov Decision Problems with Uncertain Transition Matrices

Optimal solutions to Markov Decision Problems (MDPs) are very sensitive withrespect to the state transition probabilities. In many practical problems, the estimation of those probabilities is far from accurate. Hence, estimation errors are limiting factors in applying MDPs to realworld problems.We propose an algorithm for solving finite-state and finite-action MDPs, where the solution is guaranteed to be robust with respect to estimation errors on the state transition probabilities.