Geodesic
Intelligent control system for road intersection
Problemy fiziki, matematiki i tehniki, no. 4 (2023), pp. 87-93

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An approach to the creation of intelligent object control systems using machine learning with reinforcement is illustrated using the example of an intersection control system. The simulation model of the intersection, chosen as the learning environment, is described. The results of a comparative analysis of the performance of various learning algorithms are presented. The results of applying the Monte Carlo policy gradient to train the intersection model are presented.
Keywords: transport network, reinforcement learning, neural networks, throughput, security, control systems, policy gradient. 
@article{PFMT_2023_4_a14,
     author = {E. I. Sukach and M. V. Biza},
     title = {Intelligent control system for road intersection},
     journal = {Problemy fiziki, matematiki i tehniki},
     pages = {87--93},
     publisher = {mathdoc},
     number = {4},
     year = {2023},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/PFMT_2023_4_a14/}
}
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E. I. Sukach; M. V. Biza. Intelligent control system for road intersection. Problemy fiziki, matematiki i tehniki, no. 4 (2023), pp. 87-93. http://geodesic.mathdoc.fr/item/PFMT_2023_4_a14/