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