Abstract
The purpose of the reinforcement learning system is to learn an optimal policy in general. On the other hand, in two-player games such as Othello, it is important to acquire a penalty-avoiding policy that can avoid losing the game. We know the penalty avoiding rational policy making algorithm (PARP) to learn the policy. If we apply PARP to large-scale problems, we are confronted with an explosion of the number of states. In this article, we focus on Othello, a game that has huge state spaces. We introduce several ideas and heuristics to adapt PARP to Othello. We show that our learning player beats the well-known Othello program, KITTY.
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Miyazaki, K., Tsuboi, S. & Kobayashi, S. Development of a reinforcement learning system to play Othello. Artificial Life and Robotics 7, 177–181 (2004). https://doi.org/10.1007/BF02471202
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DOI: https://doi.org/10.1007/BF02471202