  Formation and collision avoidance abilities are essential for multi-agent
systems. Conventional methods usually require a central controller and global
information to achieve collaboration, which is impractical in an unknown
environment. In this paper, we propose a deep reinforcement learning (DRL)
based distributed formation control scheme for autonomous vehicles. A modified
stream-based obstacle avoidance method is applied to smoothen the optimal
trajectory, and onboard sensors such as Lidar and antenna arrays are used to
obtain local relative distance and angle information. The proposed scheme
obtains a scalable distributed control policy which jointly optimizes formation
tracking error and average collision rate with local observations. Simulation
results demonstrate that our method outperforms two other state-of-the-art
algorithms on maintaining formation and collision avoidance.
