10 citations · 15 across the 4 of their papers we have counts for
4 papers
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…
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…
Unveiling the Decision-Making Process in Reinforcement Learning with Genetic Programming
Manuel Eberhardinger, Florian Rupp, Johannes Maucher +1
Despite tremendous progress, machine learning and deep learning still suffer from incomprehensible predictions. Incomprehensibility, however, is not an option for the use of (deep)…
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,…