  We present a novel algorithm (DeepMNavigate) for global multi-agent
navigation in dense scenarios using deep reinforcement learning (DRL). Our
approach uses local and global information for each robot from motion
information maps. We use a three-layer CNN that takes these maps as input to
generate a suitable action to drive each robot to its goal position. Our
approach is general, learns an optimal policy using a multi-scenario,
multi-state training algorithm, and can directly handle raw sensor measurements
for local observations. We demonstrate the performance on dense, complex
benchmarks with narrow passages and environments with tens of agents. We
highlight the algorithm's benefits over prior learning methods and geometric
decentralized algorithms in complex scenarios.
