5 papers
Coordinated Strategies in Realistic Air Combat by Hierarchical Multi-Agent Reinforcement Learning
Ardian Selmonaj, Giacomo Del Rio, Adrian Schneider +1
Achieving mission objectives in a realistic simulation of aerial combat is highly challenging due to imperfect situational awareness and nonlinear flight dynamics. In this work, we…
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…
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics
Ardian Selmonaj, Alessandro Antonucci, Adrian Schneider +2
Artificial intelligence (AI) is reshaping strategic planning, with Multi-Agent Reinforcement Learning (MARL) enabling coordination among autonomous agents in complex scenarios. How…
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…