Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning
This work addresses the manual effort in game development for asymmetric multiplayer content, though it is incremental as it extends an existing RL method.
The paper tackles the problem of balancing game levels for asymmetric player archetypes by using reinforcement learning to generate levels where players with different abilities have equal winning chances, demonstrating that it balances a larger proportion of levels compared to baselines but requires more training steps and loses accuracy as disparity increases.
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.