The invention relates to the technical field of traffic signal control, and provides a traffic signal control method based on reinforcement learning of a maximum entropy framework, and the method comprises the steps: constructing a simulation environment; constructing a reinforcement learning method based on a maximum entropy framework, and training an intelligent agent in the simulation environment through the reinforcement learning method based on the maximum entropy framework; and traffic signals are controlled through the trained intelligent agent.
本发明涉及交通信号控制技术领域,提出一种基于最大熵框架的强化学习的交通信号控制方法,包括:构造仿真环境;构造基于最大熵框架的强化学习方法,并且在所述仿真环境中通过所述基于最大熵框架的强化学习方法对智能体进行训练;以及通过训练后的智能体控制交通信号。
Traffic signal control method based on reinforcement learning of maximum entropy framework
基于最大熵框架的强化学习的交通信号控制方法
2023-01-10
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
Reinforcement learning-based traffic signal control
IEEE | 2024
|Deep Reinforcement Learning-Based Traffic Signal Control
ASCE | 2024
|Traffic signal control method based on reinforcement learning
European Patent Office | 2023
|Deep Reinforcement Learning-based Traffic Signal Control
IEEE | 2023
|