Autonomous Car Driving Based on Deep Reinforcement Learning

Zelin Zhang ()
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Zelin Zhang: Shandong University of Finance and Economics, Financial Big Data

A chapter in Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022), 2022, pp 835-842 from Springer

Abstract: Abstract Autonomous driving car is an important direction for the future automobile development. In order to make its algorithm have better learning ability and decision-making ability, this paper proposes the M_TD3 algorithm by improving the TD3 algorithm. Improve the sampling method and redivide the experience pool into temporary, success and failure experience pools, with the data structure of the binary tree for each experience as a node. Through a large number of simulation experiments, the model of this algorithm is constructed and analyzed and verified with other algorithms. It is proved that the vehicle controlled by the M_TD3 algorithm has a higher running speed and has a guarantee of high safety and high comfort, besides the experiment verified the feasibility of this model.

Keywords: deep reinforcement learning; autonomous driving; twin delay depth certainty gradient strategy algorithm (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-052-7_95

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DOI: 10.2991/978-94-6463-052-7_95

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