Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning
Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning
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 …