[13] Q. Vu, "The operator equation AX 0 XB = C with unbounded operators A and B and related abstract Cauchy problems," Mathematische Zeitschrift, vol. 208, pp. 567-588, 1991. Hyeong Soo Chang, Hong-Gi Lee, Michael C. Fu, and Steven I. Marcus Abstract-We propose a novel algorithm called evolutionary policy iteration (EPI) for solving infinite horizon discounted reward Markov decision processes. EPI inherits the spirit of policy iteration but eliminates the need to maximize over the entire action space in the policy improvement step, so it should be most effective for problems with very large action spaces. EPI iteratively generates a "population" or a set of policies such that the performance of the "elite policy" for a population monotonically improves with respect to a defined fitness function. EPI converges with probability one to a population whose elite policy is an optimal policy. EPI is naturally parallelizable and along this discussion, a distributed variant of PI is also studied. Index Terms-(Distributed) policy iteration, evolutionary algorithm, genetic algorithm, Markov decision process, parallelization.
Chang et al. REF introduced evolutionary policy iteration, where the policy improvement step is integrated with an EA to iteratively obtain better policies.
Evolutionary policy iteration for solving Markov decision processes
IEEE Transactions on Automatic Control
IEEE Transactions on Automatic Control
Mathematics Computer Science