  In this paper, we present a learning approach to goal assignment and
trajectory planning for unlabeled robots operating in 2D, obstacle-filled
workspaces. More specifically, we tackle the unlabeled multi-robot motion
planning problem with motion constraints as a multi-agent reinforcement
learning problem with some sparse global reward. In contrast with previous
works, which formulate an entirely new hand-crafted optimization cost or
trajectory generation algorithm for a different robot dynamic model, our
framework is a general approach that is applicable to arbitrary robot models.
Further, by using the velocity obstacle, we devise a smooth projection that
guarantees collision free trajectories for all robots with respect to their
neighbors and obstacles. The efficacy of our algorithm is demonstrated through
varied simulations.
