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We explore space traffic management as an application of collision-freenavigation in multi-agent systems where vehicles have limited observation andcommunication ranges. We investigate the effectiveness of transferring acollision avoidance multi-agent reinforcement (MARL) model trained on a groundenvironment to a space one. We demonstrate that the transfer learning modeloutperforms a model that is trained directly on the space environment.Furthermore, we find that our approach works well even when we consider theperturbations to satellite dynamics caused by the Earth's oblateness. Finally,we show how our methods can be used to evaluate the benefits ofinformation-sharing between satellite operators in order to improvecoordination.

Satellite Navigation and Coordination with Limited Information Sharing