A deep reinforcement learning-based intelligent collision-avoidance method for a swarm of unmanned surface vehicles (USVs), which belongs to the technical fields of deep reinforcement learning systems and USV intelligent collision-avoidance. Provided is an intelligent collision-avoidance method for an intelligent USV system. First, a deep reinforcement learning-based autonomous learning collision-avoidance theoretical framework for a swarm of USVs is proposed, and memory capabilities of an LSTM neural network are merged to achieve the continuity of collision-avoidance motions. Then, a design representation method for a USV environment in the framework is obtained, such as an environmental observation value, and a USV collision-avoidance reward and punishment function is proposed to determine a collision-avoidance effect. Finally, an intelligent collision-avoidance deep reinforcement learning training system for the swarm of USVs is formed. By means of simulation and verification, it is shown that trained USVs can safely navigate and implement intelligent collision-avoidance in a swarm of USV collision-avoidance environment.
DEEP REINFORCEMENT LEARNING-BASED INTELLIGENT COLLISION-AVOIDANCE METHOD FOR SWARM OF UNMANNED SURFACE VEHICLES