Robust topological policy iteration for infinite horizon bounded Markov Decision Processes.

Abstract Markov Decision Processes (mdp s) are commonly used to solve sequential decision problems. A less restrictive model is the Bounded-parameter mdp (bmdp) that allows: (i) the transition function to be expressed in terms of probability intervals and (ii) reasoning about a robust solution, i.e., the best solution under the worst model. In this paper, we propose the Robust Topological Policy Iteration (rtpi) algorithm which is a new policy iteration algorithm for infinite horizon bmdp s based on a partition of the state space. The empirical results show that the more structured the domain, the better is the performance of rtpi. [ABSTRACT FROM AUTHOR]