  Optical Earth observation satellites acquire images worldwide , covering up
to several million square kilometers every day. The complexity of scheduling
acquisitions for such systems increases exponentially when considering the
interoperabil-ity of several satellite constellations together with the
uncertainties from weather forecasts. In order to deliver valid images to
customers as fast as possible, it is crucial to acquire cloud-free images.
Depending on weather forecasts, up to 50% of images acquired by operational
satellites can be trashed due to excessive cloud covers, showing there is room
for improvement. We propose an acquisition scheduling approach based on Deep
Reinforcement Learning and experiment on a simplified environment. We find that
it challenges classical methods relying on human-expert heuristic.
