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.

Additional Metadata
Keywords Markov decision processes, Othello, Q-learning, artificial intelligence, dynamic programming, game playing, gaming, multiagent learning, neural networks, reinforcement learning
Persistent URL hdl.handle.net/1765/7142
Series Econometric Institute Research Papers
Journal Report / Econometric Institute, Erasmus University Rotterdam
Citation
van Wezel, M.C, & van Eck, N.J.P. (2005). Reinforcement learning and its application to Othello (No. EI 2005-47). Report / Econometric Institute, Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/7142