The invention provides a multi-agent reinforcement learning traffic signal control method based on strategy multiplexing, and belongs to the technical field of traffic signal control. The method comprises the following steps: constructing a universal EDLight model of the intelligent agent; the general road network model is trained based on an existing TOD scene, and a learned environment model is stored; calculating the similarity between the unknown TOD target environment and the pre-training task environment; and based on the similarity, selecting a pre-training model of a similar environment to guide a target agent model to make a decision, and updating a target domain model based on probability sampling to realize autonomous decision. The optimal strategy is selected for the unknown target network model, so that the agent reinforcement learning model can migrate in the road network and across the road network. The problem that an existing model is insufficient in traffic signal control migration capacity is solved.
本发明提供一种基于策略复用的多智能体强化学习交通信号控制方法,属于交通信号控制技术领域。通过步骤:构建智能体通用EDLight模型;基于现有TOD场景对通用路网模型进行训练,并存储已学习环境模型;计算未知TOD目标环境和预训练任务环境的相似度;基于所述相似度,选择相似环境的预训练模型来指导目标智能体模型进行决策,并基于概率采样更新目标域模型,实现自主决策。实现了为未知目标网络模型选择最优策略,从而实现了智能体强化学习模型能够在路网中和跨路网迁移。解决了现有模型对交通信号控制迁移能力不足的问题。
Multi-agent reinforcement learning traffic signal control method based on strategy multiplexing
一种基于策略复用的多智能体强化学习交通信号控制方法
2024-12-10
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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