The invention provides a method based on clustering reinforcement learning and used for optimizing traffic signals of urban road intersections, and relates to the technical field of intelligent optimization. The method comprises the following steps: step 1, defining a reinforcement learning subject, traffic states, a control action and return; step 2, acquiring traffic data for clustering use; step 3, clustering the traffic states; step 4, deciding the control action every other unit interval according to the Q value function, updating a Q value function and recording data; step 5, executing step 8 if preset learning time is exceeded, or executing step 6 otherwise; step 6, executing step 7 if re-clustering time is up, or returning to step 4 otherwise; step 7, increasing or decreasing the center of mass according to the recorded data, clustering the traffic states acquired after last clustering, and executing step 4; step 8, deciding the control action every other unit interval according to the Q value function for execution by an intersection machine. The method can increase the number of vehicles passing the road intersections within unit time.
Method based on clustering reinforcement learning and used for optimizing traffic signals of urban road intersections
2015-12-02
Patent
Elektronische Ressource
Englisch
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