[2M5-GS-10] AI application

[2M5-GS-10-01] Adaptation of Self-Play with Deep Reinforcement Learning in Puyo-Puyo

〇Kota Fukuchi¹, Youichiro Miyake¹ (1. Rikkyo University, Graduate School of Artificial Intelligence and Sciences)

Keywords:Game AI, Self-play, Puyo-Puyo

In recent years, acquisition of strategies has been successfully achieved in video games as well as board games by using self-play. In this research, we report on a study of strategy learning in single player and competitive falling-puzzle game Puyo-Puyo using self-play and deep reinforcement learning. Self-Play is a method in which agents play against each other. In this experiment, we created a puzzle game environment using Unity and ML-Agents and trained using the deep reinforcement learning algorithm SAC. The single player Puyo-Puyo was evaluated on cumulative rewards and maximum number of chains. Although there was a temporary improvement in performance, the result was a little worse. In the competitive Puyo-Puyo was evaluated on Elo-Rating and maximum number of chains. Elo-Rating increased from 1200 to 3100 and it was on an upward trend. It is possible that future studies will make it stronger.