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Reinforcement learning and its application to Othello

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  • van Wezel, M.C.
  • van Eck, N.J.P.

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.

Suggested Citation

  • van Wezel, M.C. & van Eck, N.J.P., 2005. "Reinforcement learning and its application to Othello," Econometric Institute Research Papers EI 2005-47, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:7142
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    References listed on IDEAS

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    1. Tapas K. Das & Abhijit Gosavi & Sridhar Mahadevan & Nicholas Marchalleck, 1999. "Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning," Management Science, INFORMS, vol. 45(4), pages 560-574, April.
    2. Gosavi, Abhijit, 2004. "Reinforcement learning for long-run average cost," European Journal of Operational Research, Elsevier, vol. 155(3), pages 654-674, June.
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