Template-Type: ReDIF-Paper 1.0
Author-Name: van Wezel, M.C.
Author-Name-Last: van Wezel
Author-Name-First: Michiel
Author-Name: van Eck, N.J.P.
Author-Name-Last: van Eck
Author-Name-First: Nees Jan
Title: Reinforcement learning and its application to Othello
Abstract: In this article we describe reinforcement learning, a machine learning technique for solving sequential decision problems. We describe how reinforcement learning can be combined with function approximation to get approximate solutions for problems with very large state spaces.  One such problem is the board game Othello, with a state space size of approximately 1028. We apply reinforcement learning to this problem via a computer program that learns a strategy (or policy) for Othello by playing against itself. The reinforcement learning policy is evaluated against two standard strategies taken from the literature with favorable results.  We contrast reinforcement learning with standard methods for solving sequential decision problems and give some examples of applications of reinforcement learning in operations research and management science from the literature.
Creation-Date: 2005-12-06
File-URL: https://repub.eur.nl/pub/7142/ei2005-47.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2005-47
Keywords: Markov decision processes, Othello, Q-learning, artificial intelligence, dynamic programming, game playing, gaming, multiagent learning, neural networks, reinforcement learning
Handle: RePEc:ems:eureir:7142