An Evolutionary Random Policy Search Algorithm for Solving Markov Decision Processes

An Evolutionary Random Policy Search Algorithm for Solving Markov Decision Processes

0.00 Avg rating0 Votes
Article ID: iaor200916821
Country: United States
Volume: 19
Issue: 2
Start Page Number: 161
End Page Number: 174
Publication Date: Apr 2007
Journal: INFORMS Journal On Computing
Authors: , , ,
Keywords: heuristics
Abstract:

This paper presents a new randomized search method called evolutionary random policy search (ERPS) for solving infinite–horizon discounted–cost Markov–decision–process (MDP) problems. The algorithm is particularly targeted at problems with large or uncountable action spaces. ERPS approaches a given MDP by iteratively dividing it into a sequence of smaller, random, sub–MDP problems based on information obtained from random sampling of the entire action space and local search. Each sub–MDP is then solved approximately by using a variant of the standard policy–improvement technique, where an elite policy is obtained. We show that the sequence of elite policies converges to an optimal policy with probability one. Some numerical studies are carried out to illustrate the algorithm and compare it with existing procedures.

Reviews

Required fields are marked *. Your email address will not be published.