Symmetries and Model Minimization in Markov Decision Processes
Current solution and modelling approaches to Markov Decision Processes (MDPs) scale poorly with the size of the MDP. Model minimization methods address this issue by exploiting redundancy in problem specification to reduce the size of the MDP model. Symmetries in a problem specification can give rise to special forms of redundancy that are not exploited by existing minimization methods. In this work we extend the model minimization framework proposed by Dean and Givan to include symmetries. We base our framework on concepts derived from finite state automata and group theory.