  Coordinated defensive escorts can aid a navigating payload by positioning
themselves in order to maintain the safety of the payload from obstacles. In
this paper, we present a novel, end-to-end solution for coordinating an escort
team for protecting high-value payloads. Our solution employs deep
reinforcement learning (RL) in order to train a team of escorts to maintain
payload safety while navigating alongside the payload. This is done in a
distributed fashion, relying only on limited range positional information of
other escorts, the payload, and the obstacles. When compared to a state-of-art
algorithm for obstacle avoidance, our solution with a single escort increases
navigation success up to 31%. Additionally, escort teams increase success rate
by up to 75% percent over escorts in static formations. We also show that this
learned solution is general to several adaptations in the scenario including: a
changing number of escorts in the team, changing obstacle density, and changes
in payload conformation. Video: https://youtu.be/SoYesKti4VA.
