  Finding feasible, collision-free paths for multiagent systems can be
challenging, particularly in non-communicating scenarios where each agent's
intent (e.g. goal) is unobservable to the others. In particular, finding time
efficient paths often requires anticipating interaction with neighboring
agents, the process of which can be computationally prohibitive. This work
presents a decentralized multiagent collision avoidance algorithm based on a
novel application of deep reinforcement learning, which effectively offloads
the online computation (for predicting interaction patterns) to an offline
learning procedure. Specifically, the proposed approach develops a value
network that encodes the estimated time to the goal given an agent's joint
configuration (positions and velocities) with its neighbors. Use of the value
network not only admits efficient (i.e., real-time implementable) queries for
finding a collision-free velocity vector, but also considers the uncertainty in
the other agents' motion. Simulation results show more than 26 percent
improvement in paths quality (i.e., time to reach the goal) when compared with
optimal reciprocal collision avoidance (ORCA), a state-of-the-art collision
avoidance strategy.
