Procedural Level Balancing in Runner Games
Rubem Jose Vasconcelos De Medeiros, Tacio Filipe Vasconcelos De Medeiros · 2014
Balancing a game is a long process and relies mainly on subjective feedback from human testers and selective interpretation from game developers. As a first step for completely automate game balancing, we propose a methodology to algorithmically choose features and calibrate their parameters for the procedural level generation of a simple runner game based on testers' feedback.This methodology is used in a 30 seconds game demo with survey and each playthrough is recorded and fed to a reinforcement learning algorithm. We show that the average fun grade steadily grows, proving the effectiveness of the proposed method. The collected data can be further analysed for insights on new features and other major changes.