Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning

Abstract

Balancing games, especially those with asymmetric multiplayer content, requires significant manual effort and extensive human playtesting during development. For this reason, this work focuses on generating balanced levels tailored to asymmetric player archetypes, where the disparity in abilities is balanced entirely through the level design. For instance, while one archetype may have an advantage over another, both should have an equal chance of winning. We therefore conceptualize game balancing as a procedural content generation problem and build on and extend a recently introduced method that uses reinforcement learning to balance tile-based game levels. We evaluate the method on four different player archetypes and demonstrate its ability to balance a larger proportion of levels compared to two baseline approaches. Furthermore, our results indicate that as the disparity between player archetypes increases, the required number of training steps grows, while the model's accuracy in achieving balance decreases.

Accepted at the ACM International Conference on the Foundations of Digital Games (FDG) 2025

References in corpus (5)

• Balancing of competitive two-player Game Levels with Reinforcement Learning
• Simulation-Driven Balancing of Competitive Game Levels with Reinforcement Learning
• GEEvo: Game Economy Generation and Balancing with Evolutionary Algorithms
• G-PCGRL: Procedural Graph Data Generation via Reinforcement Learning
• It might be balanced, but is it actually good? An Empirical Evaluation of Game Level Balancing