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Academic Journal of Environmental Biology, 2021, 2(4); doi: 10.38007/AJEB.2021.020405.

The Harm and Countermeasures of Automobile Exhaust Pollution Based on Deep Reinforcement Learning


Rajite Ragab

Corresponding Author:
Rajite Ragab

Vrije Universiteit Brussel, Belgium


With the improvement of people's living standards, the number of automobiles has increased rapidly, and the problem of exhaust pollution has become more and more serious. Automobile exhaust emits a large amount of nitrogen oxides, hydrocarbons and fine particle pollutants, resulting in enhanced atmospheric oxidation and hazy weather in cities. The purpose of this paper is to study the hazards and countermeasures of automobile exhaust pollution based on deep reinforcement learning. Combining the perception ability of deep learning and the decision-making ability of reinforcement learning, it has the ability to solve complex control problems, and tries to apply it to the energy management problem of hybrid electric vehicles. Compared with the total average emission factors of various pollutants of traditional gasoline cars, the average emission factors of CO, HC and NOX of clean cars based on deep reinforcement learning have obvious advantages compared with traditional gasoline cars, indicating that the deep reinforcement learning of clean cars Has good emission reduction performance.


Deep Reinforcement, Reinforcement Learning, Automobile Exhaust, Pollution Hazards

Cite This Paper

Rajite Ragab. The Harm and Countermeasures of Automobile Exhaust Pollution Based on Deep Reinforcement Learning. Academic Journal of Environmental Biology (2021), Vol. 2, Issue 4: 36-43. https://doi.org/10.38007/AJEB.2021.020405.


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