Abstract:
The real world is full of problems with multiple conflicting objectives. However, reinforcement learning traditionally deals with only a single learning objective. Recent...Show MoreMetadata
Abstract:
The real world is full of problems with multiple conflicting objectives. However, reinforcement learning traditionally deals with only a single learning objective. Recently, several multi-objective reinforcement learning algorithms have been proposed. Nevertheless, many of these algorithms rely on tabular representations of the value function which are only suitable for solving small-scaled problems. To address this limitation, various learning classifier systems have been developed to learn a scalable representation in the form of a population of classifiers. Among all learning classifier systems, XCS has been most popularly used for tackling single-objective reinforcement learning problems. Aimed at achieving multi-objective learning, a new algorithm has been developed in this paper based on XCS. Our algorithm is designed to learn a group of Pareto optimal solutions through a single learning process. For this purpose, four technical issues in XCS have been identified and addressed in this paper. Experimental studies on three bi-objective maze problems further demonstrate the effectiveness of our algorithm.
Published in: 2016 IEEE Congress on Evolutionary Computation (CEC)
Date of Conference: 24-29 July 2016
Date Added to IEEE Xplore: 21 November 2016
ISBN Information: