2 papers
cs.RO2025
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…
cs.AI2025
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…