Exploration of Multi-Agent Reinforcement Learning for ISR Flight Path Planning

Intelligence, Surveillance, and Reconnaissance (ISR) management currently involves human-operated processes that manage extensive raw data processing and analysis. However, due to time constraints, the optimization of routes and data collection often does not receive adequate attention. The scale, complexity, and intensity of future conflicts are likely to exacerbate these challenges for ISR. Intellection, a board game designed for ISR training, serves as a testing environment for flight path planning simulation. Air Force intelligence personnel, who typically play the game, must plan routes within a set timeframe. The game features predetermined and emerging collection points, challenging players to strategically deploy collection assets to maximize point acquisition. The current ISR management method faces challenges due to inefficiencies in route optimization and data collection. Traditional approaches heavily rely on human decision-making, resulting in suboptimal results. There is a pressing need for automated systems that can enhance planning efficiency while reducing time and resource requirements.