Scheduling Mission Reconfiguration for an Interferometry Synthetic Aperture Radar Using Deep Reinforcement Learning

This paper presents a method to effectively adapt the baseline of a synthetic aperture radar based on Deep Reinforcement Learning in distributed Earth observation missions. We describe the approach, which uses the Proximal Policy Optimization algorithm and provides initial results for a toy example built around a hypothetical mission to measure the vertical structure of forests using a formation of 7 satellites carrying L-band synthetic aperture radars. We demonstrate that using a reward function based on expected science return over time and fuel usage; our Deep Reinforcement Learning planner can create plans with positive scientific returns while minimizing fuel usage.