A Real-time Q-Learning Algorithm for Unmanned Surface Vehicle Target Tracking

On the background of motion planning problem, the application of enforcement learning method in unmanned surface vehicle target tracking is studied in this paper. The enforcement learning process and Q-learning model is analyzed, the improved algorithm is presented. The Q-learning algorithm framework which is suit for target tracking problem is implemented. This framework includes action space, state space, reward function, and reinforcement learning strategy. After that, based on the offline and online test scenes in certain and uncertain environment, the self-learning algorithm and control effectiveness are analyzed. The result shows that Q-learning algorithm framework has the ability of self-learning, could autonomous evolve action strategy, maximize reward function, and achieve real-time target tracking effectiveness. This work provides a research basis for increasing the self-learning ability of unmanned surface vehicle control system.