1 citations · 1 across the 6 of their papers we have counts for
3 papers · 1 filter
Towards Human Engagement with Realistic AI Combat Pilots
Ardian Selmonaj, Giacomo Del Rio, Adrian Schneider +1
We present a system that enables real-time interaction between human users and agents trained to control fighter jets in simulated 3D air combat scenarios. The agents are trained i…
Understanding Action Effects through Instrumental Empowerment in Multi-Agent Reinforcement Learning
Ardian Selmonaj, Miroslav Strupl, Oleg Szehr +1
To reliably deploy Multi-Agent Reinforcement Learning (MARL) systems, it is crucial to understand individual agent behaviors. While prior work typically evaluates overall team perf…
Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning
Ardian Selmonaj, Oleg Szehr, Giacomo Del Rio +3
This work presents a Hierarchical Multi-Agent Reinforcement Learning framework for analyzing simulated air combat scenarios involving heterogeneous agents. The objective is to iden…