The invention provides an urban traffic state modeling method based on hierarchical reinforcement learning and a traffic signal control method based on hierarchical reinforcement learning. The road intersection traffic signal control method based on the hierarchical reinforcement learning HRL comprises the steps that the first traffic state of a road intersection is acquired, and the first traffic state comprises a plurality of elements; generating a first plurality of weights according to the first traffic state, each weight corresponding to each of a plurality of elements of the first traffic state; generating a first quantifiable traffic state (QTS) according to the first plurality of weights and the plurality of elements; generating a traffic signal phase for the road intersection according to the first quantifiable traffic state QTS; wherein the state agent comprising the deep Q network is used for generating a first plurality of weights and a first quantifiable traffic state QTS according to the first traffic state; comprising a reinforcement learning module is used for generating a first traffic signal phase for the road intersection according to the first quantifiable traffic state QTS; the state agent and the traffic agent are respectively a relatively high level agent and a relatively low level agent in hierarchical reinforcement learning (HRL).
提供了Hierarch ica l Light:基于分层强化学习的城市交通状态建模方法以及交通信号控制方法。所提供的基于分层强化学习HRL的道路交叉口交通信号控制方法,包括:获取道路交叉口的第一交通状态,其中第一交通状态包括多个元素;根据所述第一交通状态生成第一多个权重,每个权重与所述第一交通状态的多个元素的每个对应;根据所述第一多个权重与所述多个元素生成第一可量化交通状态QTS;根据所述第一可量化交通状态QTS生成用于所述道路交叉口的交通信号相位;其中包括深度Q网络的状态智能体用于根据所述第一交通状态生成第一多个权重以及第一可量化交通状态QTS;包括强化学习模块的交通智能体用于根据所述第一可量化交通状态QTS生成用于所述道路交叉口的第一交通信号相位;所述状态智能体和所述交通智能体分别是分层强化学习HRL中的相对高层级代理和相对低层级代理。
HierachicalLight: urban traffic state modeling method based on hierarchical reinforcement learning and traffic signal control method
HierarchicalLight:基于分层强化学习的城市交通状态建模方法以及交通信号控制方法
2025-01-03
Patent
Electronic Resource
Chinese
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