Intelligent locomotive operation method and system based on deep reinforcement learning

Abstract

The invention relates to an intelligent locomotive operation method and system based on deep reinforcement learning. The system comprises a data source module, a locomotive operation environment learning module, an evaluation mechanism learning module and a control strategy learning module, the data source module provides needed data input for the locomotive operation environment learning module and the evaluation mechanism learning module, and the locomotive operation environment learning module and the evaluation mechanism learning module output an obtained specific operating environment and a reward function value to the control strategy learning module. On the basis of a deep reinforcement learning algorithm, a locomotive operation environment model takes real-time evaluation of the locomotive operation action as feedback information, by rewarding or punishing the current operation action, a reward function is fed back to the control strategy to serve as a reward evaluation value, and the control strategy is combined with the operating state to iteratively update and optimize the strategy. Accordingly, intelligent and optimized locomotive operation can be better achieved, and artificial participation is greatly reduced.

Description

technical field [0001] The invention relates to a locomotive manipulation method and system, in particular to a locomotive intelligent manipulation method and system based on deep reinforcement learning, belonging to the field of locomotive control. Background technique [0002] The automatic driving and optimal manipulation of railway locomotives play an important role in liberating manpower, reducing energy consumption, and improving locomotive punctuality and safety. Due to the complexity of the train operating environment and numerous influencing factors, scholars from various countries have conducted a lot of research on locomotive maneuvering optimization algorithms, which can be roughly divided into three categories: analytical solution methods, numerical optimization methods, and heuristic optimization algorithms. In the application of the analytical solution method, it is generally divided into two types: one is applied to locomotives whose input traction and brakin...

AI Technical Summary

Problems solved by technology

These studies have relied on manual driving experience to a certain extent, and realized locomotive optimal maneuvering through expert systems supplemented by machine learning, but there are still too many manual participation and it is difficult to guarantee the optimization effect

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