Multi-intersection signal lamp cooperative control method based on deep reinforcement learning

A multi-intersection, collaborative control technology, applied in the traffic control system of road vehicles, traffic control system, traffic flow detection, etc., can solve the problems of low efficiency of reinforcement learning training, deep neural network consumption, large computing resources, etc., to achieve The effect of improving model accuracy and convergence speed, simple structure, and improving operating efficiency

Active Publication Date: 2019-07-26
TSINGHUA UNIV
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This method is characterized by complete information and will not cause loss of information. However, due to the low training eff

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  • Multi-intersection signal lamp cooperative control method based on deep reinforcement learning
  • Multi-intersection signal lamp cooperative control method based on deep reinforcement learning
  • Multi-intersection signal lamp cooperative control method based on deep reinforcement learning

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Embodiment 1

[0118]The model of the present invention combines two training methods, and the specific details are shown in Algorithm 1. When it needs to be emphasized, the new policy network in the reinforcement learning of the present invention is the same network as the policy network in the imitation learning. In training, first use imitation learning to imitate several times until the accuracy Acc reaches a threshold ξ, and then use reinforcement learning for further training. In the model corresponding to the single intersection experimental environment of the present invention, ξ=0.9 is taken. In the multi-intersection environment, since the expert strategy adopted in the present invention does not consider the coordination of multi-intersections, the present invention sets ξ=0.7 to encourage exploration.

[0119] Algorithm 1 A signal light control model that combines imitation learning and reinforcement learning

[0120] Initialize a new policy network π′ and old policy network π ...

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Abstract

The invention relates to a multi-intersection signal lamp cooperative control method based on deep reinforcement learning, which comprises the following steps of: 1) establishing a multi-channel tensor capable of reflecting the original traffic state of the traffic network according to the actual condition of the multi-intersection traffic network; 2) establishing a multi-intersection cooperativecontrol neural network model according to the obtained multi-channel tensor of the multi-intersection traffic network; 3) training the established multi-intersection cooperative control neural networkmodel by adopting a method of combining simulation learning and reinforcement learning to obtain a trained multi-intersection cooperative control neural network model; and 4) inputting the phase information of the current multi-intersection into the trained multi-intersection cooperative control neural network model to obtain a cooperative control output result of the current multi-intersection signal lamp. The method can be widely applied to the field of multi-intersection signal lamp cooperative control.

Description

technical field [0001] The present invention relates to the technical field of collaborative control of multi-intersection signal lights in traffic road networks, in particular to a method for collaborative control of multi-intersection signal lights based on deep reinforcement learning. Modeling is carried out to form a new multi-intersection signal light collaborative control scheme. Background technique [0002] As the hub and key node of the urban traffic network, the intersection has a decisive influence on the operation efficiency of the traffic network. Therefore, optimizing the phase duration of intersection signal lights can greatly improve the operational efficiency of the existing urban traffic network. With the continuous development of intelligent transportation-related technologies such as cloud computing and 5G, real-time control of the transportation network through the central control system has become more and more feasible. Vehicle-to-vehicle (V2V) and v...

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Application Information

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IPC IPC(8): G08G1/01G08G1/081G08G1/095G08G1/096G06N3/04
CPCG08G1/081G08G1/0125G08G1/012G08G1/095G08G1/096G06N3/045
Inventor 胡坚明霍雨森裴欣张佐姚丹亚
Owner TSINGHUA UNIV
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