Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning

• Published in: 2017 IEEE International Conference on Robotics and Automation (ICRA)
• Main Authors: Chen, Yu Fan, Liu, Miao, Everett, Michael F, How, Jonathan P
• Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology. Department of Mechanical Engineering, Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Institute of Electrical and Electronics Engineers (IEEE) 2018
• Subjects: Orca
• Online Access: http://hdl.handle.net/1721.1/114720

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% 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. Ford Motor Company