Balancing of competitive two-player Game Levels with Reinforcement
  Learning
Florian Rupp Manuel Eberhardinger Kai Eckert
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 show 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.