Schedule Earth Observation satellites with Deep Reinforcement Learning

Requests come in a variety of size and constraints, from the urgent monitoring of small areas to large area coverage. In this work we are particularly interested in the latter case, with requests covering whole countries or even continents. Depending on weather conditions, such requests may take several months to complete, even with multiple satellites. In order to shorten the time required to fulfill requests, the mission orchestrator shall schedule acquisitions with both a short and a long-term strategy. Determining a strategy robust to an uncertain environment is a complex task, this is why current solutions mainly consist of heuristics configured by human-experts. This paper demonstrates that Reinforcement Learning (RL) might be well-suited for such a challenge. RL has proven to be of great value since these algorithms have mastered several games such as Pong on Atari 2600 (Mnih et al. 2013), Go with AlphaGo (Silver et al. 2017) and more recently Starcraft (Arulkumaran, Cully, and Togelius 2019).c