Efficient Learning of Othello Utilizing the Concept of ''Undo''

Abstract

Combination of Monte Carlo Tree Search (MCTS) and deep reinforcement learning represented as methods such as AlphaZero has achieved incredible performance, while it requires high computation resources and much training time. In this study, we propose a novel MCTS-based algorithm, where we introduce ``failure rate'' to facilitate efficient exploration and hence it shortens training time. This algorithm makes the agent prioritize the exploration of the states that are important to winning. Our method has outperformed AlphaZero in the first few iterations.