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An adaptive traffic signal control method based on multi-agent reinforcement learning

A technology of reinforcement learning and traffic signals, applied in the traffic control system of road vehicles, traffic control systems, instruments, etc., can solve problems such as incoordination, instability, and difficulty in learning cooperative strategies, so as to reduce the difficulty of training , improve the probability, reduce the effect of time delay and overhead

Active Publication Date: 2021-07-27
DALIAN MARITIME UNIVERSITY
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Problems solved by technology

Considering that the multi-agent cooperation problem will encounter difficulties such as instability and incoordination in the independent algorithm, it is difficult to learn in a complex road network environment, such as an environment with high coordination requirements between intersections caused by a large number of vehicles in the road network. good collaboration strategy

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  • An adaptive traffic signal control method based on multi-agent reinforcement learning
  • An adaptive traffic signal control method based on multi-agent reinforcement learning
  • An adaptive traffic signal control method based on multi-agent reinforcement learning

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Embodiment Construction

[0034] The present invention establishes a weak collaborative traffic model that uses independent learning agent to control the phase selection of traffic lights of each intersection, which can observe the road conditions of the intersection. The traffic model uses a simple state definition and reduces the optimization target of the independent smart body to a local area including the neighbor intersection. In response to this model, the present invention proposes a separate collaborative enhancement learning algorithm-Cooperative IMDEPENT LENIENTDOUBLE DQN (CIL-DDQN), which borrows independent Q-Learning and a large degree of idea on the DDQN algorithm to improve independent intelligence The coordination ability is between. The specific innovation of the algorithm is mainly in the following two aspects: First, forgetful experience pool, the stored experience is composed of two parts: the importance of experience and experience; the second, the algorithm loss function is defined a...

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Abstract

The invention discloses an adaptive traffic signal control method based on multi-agent reinforcement learning, comprising: for each intersection in a traffic road network, establishing an intelligent agent network corresponding to the intersection; acquiring the traffic road network The real-time traffic features in the real-time traffic features; the real-time traffic features are respectively transmitted to the agent network corresponding to the intersection according to the position of the intersection, and the phase of the intersection output by the agent network is obtained, and according to the intersection The phase of the intersection executes the traffic signal control of the intersection; wherein, the agent network is based on the average value of the sum of the number of vehicles waiting on the entry lanes of the intersection corresponding to the agent network and the intersection adjacent to the intersection Determine the phase of this intersection for the reward. The invention improves the coordination ability between independent intelligent bodies, and provides a solution for the traffic signal control in the complex road network environment.

Description

Technical field [0001] The present invention relates to the field of traffic control, and more particularly to an adaptive traffic signal control method based on multi-intelligent body strength. Background technique [0002] Implementing intelligent traffic control is a low cost method for reducing traffic congestion and improving transportation efficiency. Since traffic flow has the characteristics of time variations and randomness, the traffic modeling is still very difficult to model traffic modeling in a complex road network environment in multi-crossroads. [0003] A method of modeling a MarkovDecision Process (MDP) based on multi-intelligent body strength learning, MDP, will be a single intelligent body-critic The algorithm extends into a multi-smart transport environment. [0004] The above method is the scalable independent algorithm, only the possibility of adding the cooperation between the intelligent body from the traffic model, and does not do associated targeted des...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/081G06K9/62
CPCG08G1/081G06F18/214
Inventor 张程伟靳珊郑康洁
Owner DALIAN MARITIME UNIVERSITY
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