  Multi-robot navigation is a challenging task in which multiple robots must be
coordinated simultaneously within dynamic environments. We apply deep
reinforcement learning (DRL) to learn a decentralized end-to-end policy which
maps raw sensor data to the command velocities of the agent. In order to enable
the policy to generalize, the training is performed in different environments
and scenarios. The learned policy is tested and evaluated in common multi-robot
scenarios like switching a place, an intersection and a bottleneck situation.
This policy allows the agent to recover from dead ends and to navigate through
complex environments.
