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METHOD OF IMPLEMENTING AN INTELLIGENT TRAFFIC CONTROL APPARATUS HAVING A REINFORCEMENT LEARNING BASED PARTIAL TRAFFIC DETECTION CONTROL SYSTEM, AND AN INTELLIGENT TRAFFIC CONTROL APPARATUS IMPLEMENTED THEREBY
METHOD OF IMPLEMENTING AN INTELLIGENT TRAFFIC CONTROL APPARATUS HAVING A REINFORCEMENT LEARNING BASED PARTIAL TRAFFIC DETECTION CONTROL SYSTEM, AND AN INTELLIGENT TRAFFIC CONTROL APPARATUS IMPLEMENTED THEREBY
A method of implementing an intelligent traffic control apparatus comprising providing a traffic control apparatus with a reinforcement learning based control system for a given traffic location; training the reinforcement based control system for the given traffic location on a simulator that simulates the given traffic location in a training environment, wherein the reinforcement learning based control system receives only partial traffic detection in the training environment on the simulator; and coupling the reinforcement learning based control system to the traffic control apparatus at the given traffic location after training. Specifically, the reinforcement learning based control system to the traffic control apparatus can function with improved results over current controls when less than 80%, and generally at least 5%, of vehicles are detected. Distributed independent or interconnected traffic control apparatuses may be implemented as well as a centralized system with multiple intelligent traffic control apparatus.
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