Balancing of competitive two-player Game Levels with Reinforcement Learning
We are happy to share that the PhD students Florian Rupp and Manuel Eberhardinger got their work on balancing competitive two-player game levels with reinforcement learning accepted at the IEEE Conference on Games (IEEE CoG). The paper will be presented in Boston, 21th-24th August 2023.
The balancing process for game levels in a competitive two-player context involves a lot of manual work and testing, particularly in non-symmetrical game levels. In this paper, we propose an architecture for automated balancing of tile-based levels within the recently introduced PCGRL framework (procedural content generation via reinforcement learning). Our architecture is divided into three parts: (1) a level generator, (2) a balancing agent and, (3) a reward modeling simulation. By playing the level in a simulation repeatedly, the balancing agent is rewarded for modifying it towards the same win rates for all players. To this end, we introduce a novel family of swap-based representations to increase robustness towards playability. We how that this approach is capable to teach an agent how to alter a level for balancing better and faster than plain PCGRL. In addition, by analyzing the agent’s swapping behavior, we can draw conclusions about which tile types influence the balancing most. We test and show our results using the Neural MMO (NMMO) environment in a competitive two-player setting.
Authors: Florian Rupp, Manuel Eberhardinger, Kai Eckert