基于输出层具有噪声的DQN的无人车路径规划.

The path programming of the unmanned ground vehicle（UGV） was studied under the framework of the deep Q-network（DQN） algorithm. To improve the exploration efficiency, the DQN algorithm was applied through discretization of the continuous state into the discrete state. To balance between exploration and exploitation, the Gaussian noise was added only in the output layer of the network, and a progressive reward function was designed. Finally, experiments were carried out in the Gazebo simulation environment. The simulation results show that, first, this strategy can quickly program a collision-free route from the initial point to the target point, and the convergence speed is significantly higher than those of the Q-learning algorithm, the DQN algorithm and the noisynet＿DQN algorithm; second, this strategy has the generalization ability about the initial point, the target point and the obstacles, as well as verified effectiveness and robustness. [ABSTRACT FROM AUTHOR]