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cs.LG2025
Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning
Florian Rupp, Kai Eckert
Balancing games, especially those with asymmetric multiplayer content, requires significant manual effort and extensive human playtesting during development. For this reason, this…
cs.LG2025
Simulation-Driven Balancing of Competitive Game Levels with Reinforcement Learning
Florian Rupp, Manuel Eberhardinger, Kai Eckert
The balancing process for game levels in competitive two-player contexts involves a lot of manual work and testing, particularly for non-symmetrical game levels. In this work, we f…
cs.LG2024
G-PCGRL: Procedural Graph Data Generation via Reinforcement Learning
Florian Rupp, Kai Eckert
Graph data structures offer a versatile and powerful means to model relationships and interconnections in various domains, promising substantial advantages in data representation,…