How can RL optimize multi-agent drone tracking?
Cooperative Search and Track of Rogue Drones using Multiagent Reinforcement Learning
This paper proposes a multi-agent reinforcement learning (MARL) system for a team of UAVs to search and track rogue drones. The agents learn to coordinate their movements to maximize the number of rogue drones detected within a defined area, without prior knowledge of drone locations or behavior. Key to the system is a "difference reward" function, which improves learning speed and scalability compared to a standard global reward by quantifying each agent's individual contribution. This approach is relevant to LLM-based multi-agent systems by demonstrating a successful application of MARL for a complex cooperative task, using a reward shaping technique that could be adapted for LLM agents to improve their coordinated learning in collaborative scenarios.