Distributed Signal Control of Multi-agent Reinforcement Learning Based on Game

被引：0
|
作者：
Qu Z.-W. [1 ]
Pan Z.-T. [1 ]
Chen Y.-H. [1 ]
Li H.-T. [1 ]
Wang X. [1 ]
机构：
[1] College of Transportation, Jilin University, Changchun
来源：
Chen, Yong-Heng (cyh@jlu.edu.cn) | 1600年 / Science Press卷 / 20期
基金：
中国国家自然科学基金;
关键词：
Distributed traffic signal control; Game theory; Intelligent transportation; Multi-agent reinforcement learning; Numerical simulation; Urban road network under unbalanced demand;
D O I：
10.16097/j.cnki.1009-6744.2020.02.012
中图分类号：
学科分类号：
摘要：
The difficulty of distributed signal control is increasing due to the unbalance and fluctuation of traffic demand. Since the decision-making of existing independent action multi-agent reinforcement learning (IA-MARL) is based on its own historical experience, the distributed signal control based on IA-MARL is difficult to timely alleviate the impact of unbalanced and fluctuating traffic demand. In this paper, the framework of multi-agent reinforcement learning based on the game (G- MARL) was proposed by improving the decision-making of IA-MARL with integrating the mixed strategy Nash-equilibrium, which is a concept in game theory. In the grid network with the Poisson arrival rate, the distributed control methods based on IA-MARL and G-MARL were simulated to obtain the unit travel time and the unit vehicle delay curves. The results show that, the unit travel time and the unit vehicle average delay obtained by G-MARL are reduced by 59.94% and 81.45% compared with IA-MARL respectively. It is proved that G-MARL is suitable for distributed signal control when there are unbalances and fluctuations in traffic demand with the unsaturated state. Copyright © 2020 by Science Press.
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页码：76 / 82and100
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