Deep Reinforcement Learning for Autonomous Traffic Light Control

被引:0
作者
Garg, Deepeka [1 ]
Chli, Maria [1 ]
Vogiatzis, George [1 ]
机构
[1] Aston Univ, Sch Engn & Appl Sci, Birmingham, W Midlands, England
来源
2018 3RD IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION ENGINEERING (ICITE) | 2018年
关键词
component; Autonomous Traffic Control; Machine Learning; Deep Reinforcement Learning; 3d Virtual Reality Simulator;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In urban areas, the efficiency of traffic flows largely depends on signal operation and expansion of the existing signal infrastructure is not feasible due to spatial, economic and environmental constraints. In this paper, we address the problem of congestion around the road intersections. We developed our traffic simulator to optimally simulate various traffic scenarios, closely related to real-world traffic situations. We contend that adaptive real-time traffic optimization is the key to improving existing infrastructure's effectiveness by enabling the traffic control system to learn, adapt and evolve according to the environment it is exposed to. We put forward a vision-based, deep reinforcement learning approach based on a policy gradient algorithm to configure traffic light control policies. The algorithm is fed real-time traffic information and aims to optimize the flows of vehicles travelling through road intersections. Our preliminary test results demonstrate that, as compared to the traffic light control methodologies based on previously proposed models, configuration of traffic light policies through this novel method is extremely beneficial.
引用
收藏
页码:214 / 218
页数:5
相关论文
共 11 条
[1]  
[Anonymous], 2016, ABS161101142 CORR
[2]  
[Anonymous], 2009, 2009 12 INT IEEE C I
[3]  
[Anonymous], 2011, P 3 INT C ADV SYST S
[4]  
Azimi R., 2012, SAE WORLD C
[5]  
Bloomberg L., 2000, Comparison of VISSIM and CORSIM Traffic Simulation Models on a Congested Network
[6]  
BOXILL SA, 2000, 1676021 TEX SO U
[7]   A multiagent approach to autonomous intersection management [J].
Dresner, Kurt ;
Stone, Peter .
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2008, 31 :591-656
[8]   Human-level control through deep reinforcement learning [J].
Mnih, Volodymyr ;
Kavukcuoglu, Koray ;
Silver, David ;
Rusu, Andrei A. ;
Veness, Joel ;
Bellemare, Marc G. ;
Graves, Alex ;
Riedmiller, Martin ;
Fidjeland, Andreas K. ;
Ostrovski, Georg ;
Petersen, Stig ;
Beattie, Charles ;
Sadik, Amir ;
Antonoglou, Ioannis ;
King, Helen ;
Kumaran, Dharshan ;
Wierstra, Daan ;
Legg, Shane ;
Hassabis, Demis .
NATURE, 2015, 518 (7540) :529-533
[9]   Traffic light control using deep policy-gradient and value-function-based reinforcement learning [J].
Mousavi, Seyed Sajad ;
Schukat, Michael ;
Howley, Enda .
IET INTELLIGENT TRANSPORT SYSTEMS, 2017, 11 (07) :417-423
[10]   Revisiting Street Intersections Using Slot-Based Systems [J].
Tachet, Remi ;
Santi, Paolo ;
Sobolevsky, Stanislav ;
Reyes-Castro, Luis Ignacio ;
Frazzoli, Emilio ;
Helbing, Dirk ;
Ratti, Carlo .
PLOS ONE, 2016, 11 (03)