The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 21, 2021

Filed:

Feb. 26, 2021
Applicant:

East China Jiaotong University, Nanchang, CN;

Inventors:

Hui Yang, Nanchang, CN;

Yu Wang, Nanchang, CN;

Zhongqi Li, Nanchang, CN;

Yating Fu, Nanchang, CN;

Chang Tan, Nanchang, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B61D 3/08 (2006.01); G06N 3/08 (2006.01); B61L 25/02 (2006.01); G06K 9/62 (2006.01); G05B 13/04 (2006.01); G06N 5/02 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); B61L 25/021 (2013.01); B61L 25/023 (2013.01); B61L 25/028 (2013.01); G05B 13/042 (2013.01); G06K 9/6256 (2013.01); G06N 5/022 (2013.01);
Abstract

The present disclosure provides a method and system for controlling a heavy-haul train based on reinforcement learning. The method includes: obtaining operation state information of a heavy-haul train at a current time point; obtaining a heavy-haul train action of a next time point according to the operation state information of the heavy-haul train at the current time point and a heavy-haul train virtual controller, and sending the heavy-haul train action of the next time point to a heavy-haul train control unit to control operation of the heavy-haul train. The heavy-haul train virtual controller is obtained by training a reinforcement learning network according to operation state data of the heavy-haul train and an expert strategy network; the reinforcement learning network includes one actor network and two critic networks; the reinforcement learning network is constructed according to a soft actor-critic (SAC) reinforcement learning algorithm.


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