10 citations · 26 across the 8 of their papers we have counts for
4 papers · 1 filter
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)…
It might be balanced, but is it actually good? An Empirical Evaluation of Game Level Balancing
Florian Rupp, Alessandro Puddu, Christian Becker-Asano +1
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 Generatio…
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,…
GEEvo: Game Economy Generation and Balancing with Evolutionary Algorithms
Florian Rupp, Kai Eckert
Game economy design significantly shapes the player experience and progression speed. Modern game economies are becoming increasingly complex and can be very sensitive to even mino…