基于ASP-SAC算法的列车自动驾驶速度控制OA北大核心CSTPCDEI

With the green transformation of economic development and the rapid advancement of artificial intelligence,urban rail transit has become an important mode of daily travel for residents.While ensuring safety,efficiency,and punctuality,the energy-saving and comfort demands of train operation have also attracted increasing attention.Reasonable operation strategies can effectively achieve automatic driving speed control of trains under multiple control requirements.Reinforcement learning,as an intelligent decision-making method,can effectively solve this control problem.Firstly,based on the comprehensive analysis of factors such as technology,safety,and passenger experience,the Soft Actor-Critic(SAC)algorithm was improved as the Action-State Experience Prioritized Soft Actor-Critic(ASP-SAC)method,using expert experience action segmentation and state information entropy to study the problem of automatic train operation speed control.Secondly,the problem was formalized as a Markov decision process.The train operation environment was established.The state space,action space,and reward function based on goal control were determined.Finally,using a section of data from the Beijing Subway Yizhuang Line as an example,the ASP-SAC method was validated and compared with other algorithms in the same environment.The research results show that the method is feasible for automatic train operation speed control under multiple target requirements,with an efficiency improvement of 22.73%compared to the unimproved algorithm,and a 29.17%improvement compared to the PPO algorithm.Additionally,the method outperforms SAC,DQN,PPO,and PID algorithms in timeliness,precision,and energy efficiency while ensuring safety and comfort during train operation,with energy consumption reduced by 3.64%,5.62%,4.38%,and 7.35%respectively,demonstrating good control effects.Furthermore,the method can possess robustness and has certain superiority and reference value in the aspect of automatic train operation speed control.