Aiming at the traditional deep reinforcement learning algorithms'problems of simultan-eous low driving efficiency,slow convergence and low decision success rate in self-driving decision-making tasks due to poor exploration strategies during training,a decision-making method of deep competitive double Q network combined with expert evaluation is proposed.An offline expert model and an online model are proposed,and an adaptive balance factor is introduced between them;a prioritized experience replay mechanism with adaptive importance coefficients is introduced to build an online model on the basis of the competitive deep Q-network;and a reward function that considers driving efficiency,safety,and comfort is designed.The results show that the algorithm improves the convergence speed by 25.93%and 20.00%,the decision success rate by 3.19%and 2.77%,the average steps by 6.40%and 0.14%,and the average speed by 7.46%and 0.42%,respectively,compared with D3QN and PERD3QN.
维普
万方数据