Playtesting in Match 3 Game Using Strategic Plays via Reinforcement Learning

Abstract

Playtesting is a lifecycle phase in game development wherein the completeness and smooth progress of planned content are verified before release of a new game. Although studies on playtesting in Match 3 games have attempted to utilize Monte Carlo tree search (MCTS) and convolutional neural networks (CNNs), the applicability of these methods are limited because the associated training is time-consuming and data collection is difficult. To address this problem, game playtesting was performed via learning based on strategic play in Match 3 games. Five strategic plays were defined in the Match 3 game under consideration and game playtesting was performed for each situation via reinforcement learning. The proposed agent performed within a 5 & x0025; margin of human performance on the most complex mission in the experiment. We demonstrate that it is possible for the level designer to measure the difficulty of the level via playtesting various missions. This study also provides level testing standards for several types of missions in Match 3 games.