Playtesting Match 3 Games with PPO

Abstract

The training of proximal policy optimization agents with action masking on stochastic match-3 environments is explored in this thesis. A performant, feature-rich match-3 simulator is developed, and experiments demonstrate improved performance over a random policy on both seen and unseen levels. Furthermore, the best generalization performance is achieved when training is done by sampling levels from a subset of levels.