Strategy acquisition for the game "Othello" based on reinforcement learning
This article discusses automatic strategy acquisition for the game "Othello" based on a reinforcement learning scheme. In our approach, a computer player, which initially knows only the game rules, becomes stranger after playing several thousands of games against another player. In each game, the computer player refines the evaluation function for the game state, which is achieved by min-max reinforcement learning (MMRL). MMRL is a simple learning scheme that uses the minmax strategy. Since the state space of Othello is huge, we employ a normalized Gaussian network (NGnet) to represent the evaluation function. As a result, the computer player becomes strong enough to beat a player employing a heuristic strategy. This article experimentally shows that MMRL is better than TD(0) and also shows that the NGnet is better than a multi-layered perceptron, in our Othello task.
othello reinforcement learning normalized gaussian network min-max strategy