Reinforcement learning and its application to Othello

Michiel van Wezel and Nees Jan van Eck

No EI 2005-47, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

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.

Keywords: Markov decision processes; Othello; Q-learning; artificial intelligence; dynamic programming; game playing; gaming; multiagent learning; neural networks; reinforcement learning (search for similar items in EconPapers)
Date: 2005-12-06
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