TitleEvolutionary Algorithms for Reinforcement Learning
Publication TypeJournal Article
Year of Publication1999
AuthorsMoriarty, DE, Schultz, AC, Grefenstette, JJ
JournalJournal of Artificial Intelligence Research
Volume11
Pagination241-276
Date Published09/1999
Abstract

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications.

Full Text
NRL Publication Release Number: 
7-1221.1-1704
pub_tags: 
machine learning
reinforcement learning
samuel
genetic algorithms