Optimizing Stage Construction and Level Balancing of Match-3 Puzzle Game with PPO Algorithm Machine Learning

Abstract

Although the global market has witnessed a proliferation of diverse match-3 puzzle games, achieving success in this competitive market remains challenging. The crucial factors that determine the success of match-3 puzzle games are the creation of numerous engaging stages and precise level balancing. The purpose of this study is to propose a match-3 puzzle game system that aims at identifying the most effective algorithm for training artificial intelligence agents in stage construction and level balancing verification. To validate the systems' usefulness, this paper conducted experiments with the Proximal Policy Optimization (PPO) algorithm and obtained cumulative reward and entropy value graphs. Consequently, it has been confirmed that the system can be employed to compare learning outcomes for each algorithm and identify the optimal algorithm suitable for match-3 puzzle games. The use of machine learning technology in match-3 puzzle games holds the promise of revolutionizing game development and leading to the creation of more captivating and rewarding gaming experiences for players.