Name
Abstract | ||
|---|---|---|
With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20154291 | SENSORS |
Keywords | DocType | Volume |
ITS,IoT,reinforcement learning,MRAL,multi-agent,MAAC,edge computing | Journal | 20 |
Issue | ISSN | Citations |
15 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Wu | 1 | 304 | 40.42 |
| Jianqing Wu | 2 | 0 | 1.01 |
| Jun Shen | 3 | 234 | 40.40 |
| Binbin Yong | 4 | 0 | 0.34 |
| Qingguo Zhou | 5 | 103 | 29.48 |