The invention discloses a single-point intersection signal control method based on deep cycle Q learning. The method comprises the following steps: learning an optimal signal control strategy at a single intersection by using a deep cyclic Q learning (DRQN) algorithm, wherein according to the DRQN algorithm, an LSTM neural network is introduced on the basis of DQN, the characteristic that the LSTMcan memorize time axis information is utilized, the current intersection input state is comprehensively expressed by combining the states of the intersection at the previous several moments instead of the state of the intersection at the current moment and thus the influence of the POMDP characteristics of the intersection on deep Q learning performance is reduced. The performance of the improvedDRQN algorithm provided by the invention is superior to that of the DQN algorithm and is also superior to that of a traditional intersection timing control method. When the traffic flow is close to saturation and supersaturation, the DRQN algorithm can observe the state of the intersection at each moment and make an optimal opportunity choice, thereby improving the traffic efficiency of the intersection.
本发明公开了一种基于深度循环Q学习的单点交叉口信号控制方法,该方法在单个路口使用深度循环Q学习(DRQN)算法学习最优信号控制策略,其中DRQN算法在DQN的基础上引入LSTM神经网络,利用LSTM能够记忆时间轴信息的特性,通过结合交叉口前几个时刻的状态而非仅仅通过当前时刻的状态来全面地表示当前交叉口输入状态,从而减小交叉口POMDP特性对深度Q学习性能的影响,本发明改进后的DRQN算法性能优于DQN算法,也优于传统的交叉口定时控制方法;当交通流接近饱和和过饱和时,DRQN算法可以观察交叉口在各个时刻的状态,做出最佳的时机选择,从而提高交叉口通行效率。
Single-point intersection signal control method based on deep cyclic Q learning
一种基于深度循环Q学习的单点交叉口信号控制方法
2020-06-05
Patent
Elektronische Ressource
Chinesisch
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