基于强化学习的列车驾驶曲线节能优化算法

在保证安全和准时性的前提下,自动化列车运行可以有效减少列车耗能。为了灵活应对列车运行种的动态变化,提出了一种基于强化学习的方法,可以优化列车控制策略且不采用之前关于列车动力学的知识和设计的列车速度曲线。这个优化模型将列车节能作为目标,把准点到达、列车限速、停车位置作为限制条件。大量的列车运行经验可以被用来训练深度神经网络直到得到最优化行为价值函数,通过对训练过的神经网络输入状态,可以准确输出每个行为的价值,然后再根据行为价值的大小来选择最优的驾驶策略。

An Energy-efficient Train Control Approach Based on Deep Q-network Methodology

Application of the automatic train operation can efficiently reduce the energy consumption under the premise of ensuring safety and punctuality.To flexibly respond to the dynamic changes during the train operation,this paper proposes a data driven based approach,specifically a reinforcement learning(RL)approach,which can optimize the train control strategy without using the prior knowledge of train dynamics and the pre-designed velocity profile.The optimization model takes the energy consumption as objective,considering punctual arrival,speed restrictions and station parking.Numerous driving experiences are used to train the deep Q-network until it approximates the action-value function.The trained neural network can accurately output the values of each action with arbitrary input state such that the optimal control strategy can be obtained according to action values.Based on the real-world data,some cases are conducted to illustrate the effectiveness of the proposed approach.