A deep Q-network learning-based traffic light dynamic timing algorithm; in a method, a traffic control center first counts all traffic light intelligent agents and saves same in a set; the traffic control center initializes deep Q-network weights of all of the intelligent agents, performs dynamic timing according to an action control request sent by a traffic light on a road network, and then executes a timing action; the traffic control center detects in real time whether an action selected by each intelligent agent has ended, inputs an intelligent agent the selected action of which is complete into a deep Q-network according to traffic information fed back in real time, and determines a specific time for a next instance of traffic timing according to an output of the network; when determined that the action has not stopped, the control center performs dynamic timing again according to the action control request sent by the traffic light; finally, the effect of reasonably timing traffic lights to reduce traffic jams is attained.
DEEP Q-NETWORK LEARNING-BASED TRAFFIC LIGHT DYNAMIC TIMING ALGORITHM