Automated playtesting of platformer games using reinforcement learning

Abstract

Platformer games are popular in the video game industry and their design require much efforts from game companies. As part of the design process, playtesting is key for improving the gameplay before game release. Playtesting is the quality assurance phase of the game development cycle where people are hired to play the game, report bugs and provide feedback regarding the playability of the game. This feedback could be used for game balancing (process of tuning game rules to prevent them from being ineffective or provide undesirable results). However, playtesting may be expensive if done manually and may require several iterations, resulting in high budget requirement and time for game companies. In this thesis, we investigate a way to automatically playtest 2D platformer levels using a combination of deep reinforcement learning and curriculum learning, for both quality assurance and game balancing. Deep Reinforcement Learning has contributed greatly in playing games (Atari and Dota 2) and in this thesis, we will try to replicate the results to playtest games. Curriculum learning is an approach that has shown promising results thus we will explore it to derive useful results. We develop our APT tool by training an artificial intelligence (AI) agent on several different platformer levels following a curriculum, and use the trained agent to playtest newly-created levels. Our APT is able to identify areas of the level that needed design improvements and further gameplay balancing. We contribute a reliable APT tool for designers that wish to easily design 2D platformer games and a discussion of how our results extend to APT at-large.