1 citations · 1 across the 3 of their papers we have counts for
3 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…
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…
Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering
Ardian Selmonaj, Oleg Szehr, Giacomo Del Rio +3
The application of artificial intelligence to simulate air-to-air combat scenarios is attracting increasing attention. To date the high-dimensional state and action spaces, the hig…