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A Methodology Based on Deep Reinforcement Learning to Autonomous Driving with Double Q-Learning

Deep reinforcement learning (DRL) is a typical artificial intelligence algorithm utilized to solve problems in decision making, which has been proved efficient in go chess, video games and physical control. This paper introduced deep reinforcement learning to motor vehicle driving in order to achieve autonomous driving. Different from the traditional system, prediction algorithm and control algorithm were combined in this paper, generating an end-to-end autonomous driving methodology, the performance of which was discussed through experiments under CARLA simulator. Researchers adopted experience replay and deep neural network to make up the main part of the methodology, with RMS-prop algorithm and double Q-learning introduced to improve the performance of training part of autonomous driving. The methodology was utilized in an established route and was compared with simple deep learning method so that the superiority of double Q-learning methodology showed, which was of faster convergence rate and better predictive performance.