Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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We consider a discrete time Markov decision process (MDP) with a finite state space, a finite action space, and two kinds of immediate rewards. The problem is to maximize the time average reward generated by one reward stream, subject to the other reward not being smaller than a prescribed value. An MDP with a reward constraint can be solved by linear programming in the range of mixed policies. On the other hand, when we restrict ourselves to pure policies, the problem is a combinatorial problem, for which a solution has not been discovered. In this paper, we propose an approach by Genetic Algorithms (GAs) in order to obtain an effective search process and to obtain a near optimal, possibly optimal pure stationary policy. A numerical example is given to examine the efficiency of the approach proposed.