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cs.AI2025
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…
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…