Traffic organization scheme optimization method based on multi-agent reinforcement learning

Abstract

The invention discloses a traffic organization scheme optimization method based on multi-agent reinforcement learning, and the method comprises the steps: improving an Actor network in MADDPG, improving an experience library in a Critic network based on a birth and death process, employing the maximum traffic flow at an early peak as an agent return index, training a maximum entropy inverse reinforcement learning model by using trajectory data to serve as a multi-agent return mechanism, and designing a return function of reinforcement learning based on the return mechanism. According to the method, the current urban traffic organization scheme is optimized, the current traffic data are analyzed to find out the cause of traffic jam, the method can well adapt to and quickly find out the optimal scheme, traffic guidance suggestions are provided for traffic police experts, and a foundation is laid for a smart city.

Description

technical field [0001] The invention belongs to the technical field of traffic flow optimization, in particular to a traffic organization scheme optimization method based on multi-agent reinforcement learning. Background technique [0002] With the rapid development of the city, the urban road network is becoming more and more complex, and the problem of traffic congestion is becoming more and more serious. The control of vehicle flow in urban areas is not single. If the turning of a certain intersection is restricted separately, the flow of the surrounding road sections will be difficult to control and congestion will be more likely. The use of multi-agent reinforcement learning to control road traffic flow will also appear unsuitable. For example, in a complex urban road network, multi-agent teamwork and communication information is insufficient, and the efficiency of integrated learning is low; it is difficult to design a standard for traffic flow returns. ; The global l...

AI Technical Summary

Problems solved by technology

The control of vehicle flow in urban areas is not single. If you limit the turning of a certain intersection separately, the flow of the surrounding road sections will be difficult to control and more likely to be congested.

The use of multi-agent reinforcement learning to control road traffic flow will also appear unsuitable. For example, in a complex urban road network, multi-agent teamwork and communication information is insufficient, and the efficiency of integrated learning is low; it is difficult to design a standard for traffic flow returns. ; Global linkage changes in traffic flow lead to multi-agents generating a lot of experience in the process of learning, but the traditional multi-agent algorithm is relatively simple in processing experience

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