A Genetic Search in Policy Space for Solving Markov Decision Processes

Markov Decision Processes (MDPs) have been studied extensively in the context of decision making under uncertainty. This paper presents a new methodology for solving MDPs, based on genetic algorithms. In particular, the importance of discounting in the new framework is dealt with and applied to a model problem. Comparison with the policy iteration algorithm from dynamic programming reveals the advantages and disadvantages of the proposed method.