It might be balanced, but is it actually good? An Empirical Evaluation of Game Level Balancing

Abstract

Achieving optimal balance in games is essential to their success, yet reliant on extensive manual work and playtesting. To facilitate this process, the Procedural Content Generation via Reinforcement Learning (PCGRL) framework has recently been effectively used to improve the balance of existing game levels. This approach, however, only assesses balance heuristically, neglecting actual human perception. For this reason, this work presents a survey to empirically evaluate the created content paired with human playtesting. Participants in four different scenarios are asked about their perception of changes made to the level both before and after balancing, and vice versa. Based on descriptive and statistical analysis, our findings indicate that the PCGRL-based balancing positively influences players' perceived balance for most scenarios, albeit with differences in aspects of the balancing between scenarios.

4 pages, 2 figures, 1 table. Accepted at the IEEE Conference on Games (IEEE CoG) 2024

References in corpus (4)

• Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents
• 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

Cited by in corpus (1)

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