3 papers
cs.LG2026
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…
cs.AI2026
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…
cs.AI2026
Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach
Alessandro Sestini, Joakim Bergdahl, Jean-Philippe Barrette-LaPierre +5
While several high profile video games have served as testbeds for Deep Reinforcement Learning (DRL), this technique has rarely been employed by the game industry for crafting auth…