How can hierarchical RL improve multi-UAV combat coordination?
A Hierarchical Reinforcement Learning Framework for Multi-UAV Combat Using Leader-Follower Strategy
This paper proposes a hierarchical reinforcement learning framework for coordinating multiple UAVs in air combat using a leader-follower strategy. The framework allows UAVs to learn complex collaborative maneuvers, outperforming traditional methods that treat multi-UAV combat as multiple independent 1v1 engagements. A key improvement is a modified critic within the multi-agent reinforcement learning algorithm, allowing followers to prioritize the leader's actions for better coordination. This approach is relevant to LLM-based multi-agent systems as it demonstrates a method for improving agent collaboration and decision-making within a hierarchical structure, potentially applicable to complex, multi-agent scenarios beyond air combat. The target selector further refines agent focus and action selection in complex environments, relevant to prioritizing interactions within multi-agent LLM systems.