A Control Strategy of Autonomous Vehicles Based on Deep Reinforcement Learning

Wei Xia, Huiyun Li, Baopu Li

Abstract: Deep reinforcement learning has received considerable attention after the outstanding performance of AlphaGo. In this paper, we propose a new control strategy of self-driving vehicles using the deep reinforcement learning model, in which learning with an experience of professional driver and a Q-learning algorithm with filtered experience replay are proposed. Experimental results demonstrate that the proposed model can reduce the time consumption of learning by 71.2%, and the stability increases by about 32%, compared with the existing neural fitted Q-iteration algorithm.