A semi-decentralized feudal multi-agent learned-goal algorithm for multi-intersection traffic signal control
作者：
Highlights：
• A representation learning algorithm is proposed to learn the optimal latent goals.
• A learned-goal soft actor–critic algorithm is proposed as one traffic-signal agent.
• A semi-decentralized feudal multi-agent framework is proposed to lower state space.
• Based on above methods, the whole algorithm is proposed for traffic signal control.
• Experiments show the whole algorithm outperforms the state-of-the-art algorithms.
摘要
•A representation learning algorithm is proposed to learn the optimal latent goals.•A learned-goal soft actor–critic algorithm is proposed as one traffic-signal agent.•A semi-decentralized feudal multi-agent framework is proposed to lower state space.•Based on above methods, the whole algorithm is proposed for traffic signal control.•Experiments show the whole algorithm outperforms the state-of-the-art algorithms.
论文关键词：Traffic signal control,Multi-agent reinforcement learning,Hierarchical reinforcement learning,Representation learning,Feudal multi-agent framework
论文评审过程：Received 6 July 2020, Revised 22 October 2020, Accepted 17 December 2020, Available online 26 December 2020, Version of Record 26 December 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.106708
