5 citations · 17 across the 26 of their papers we have counts for
4 papers · 1 filter
Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution
Jannik Hösch, Alessandro Sestini, Florian Fuchs +6
Reinforcement learning (RL) has achieved strong performance in sequential decision-making, yet scaling to complex multi-agent environments remains challenging due to sparse rewards…
Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26
Florian Fuchs, Jessy Gosselin-Grant, Boris Skuin +5
Testing is a major effort for the gaming industry, requiring a significant part of development budget and people power. We present a case study on a development version of the ice…
Augmenting Game AI with Deep Reinforcement Learning
Alessandro Sestini, Joakim Bergdahl, Amir Baghi +3
Immersion in video games depends not only on graphics, audio, and game mechanics, but also on the quality of in-game characters. Producing believable characters, or game AI, remain…
SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data
Carlo Romeo, Girolamo Macaluso, Alessandro Sestini +1
Incorporating prior data into online reinforcement learning accelerates training but typically forces a difficult trade-off between high computational costs and long, multi-stage t…