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Evaluation of Reinforcement Learning Traffic Signalling Strategies for Alternative Objectives: Implementation in the Network of Nicosia, Cyprus

Haris Ballis, Loukas Dimitriou
2020 Transport and Telecommunication  
More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies  ...  The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.  ...  Acknowledgements This paper is a revised and expanded version of the paper "Evaluating the Performance of Reinforcement Learning Signalling Strategies for Sustainable Urban Road Networks" presented at  ... 
doi:10.2478/ttj-2020-0024 fatcat:pz65e4lp4zebzg7gpf77tzew5q

Learning Techniques in a Mobile Network [chapter]

Sidi-Mohammed Senouci
2013 Autonomic Networks  
presents a new call admission control method in cellular networks based on reinforcement learning; section 11.4 discusses a new dynamic radio resource allocation policy in cellular systems, also based  ...  Call admission control presented in this section enables this type of mechanism. It is obtained by using the reinforcement learning algorithm Q-learning which was presented in the previous section.  ... 
doi:10.1002/9780470610879.ch11 fatcat:h76ctzchkrgc7bjkhx2usbygke

Deep Reinforcement Learning for NFV-based Service Function Chaining in Multi-Service Networks : Invited Paper

Zili Ning, Ning Wang, Rahim Tafazolli
2020 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR)  
Under such a multi-service network environment, we consider the co-existence of heterogeneous traffic control mechanisms, including flexible, dynamic service function chaining (SFC) traffic control and  ...  Towards this end, we propose a deep reinforcement learning based scheme to enable intelligent SFC routing decision-making in dynamic network conditions.  ...  The authors would also like to acknowledge the support of the University of Surrey's 5G Innovation Centre (5GIC) ( members for this work.  ... 
doi:10.1109/hpsr48589.2020.9098994 dblp:conf/hpsr/NingWT20 fatcat:wqcdjz55ejh73jcjeh6qedwus4

Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning

Wen-Long Shang, Yanyan Chen, Xingang Li, Washington Y. Ochieng, Zhile Yang
2020 Complexity  
This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, to improve  ...  The results show that the proposed adaptive signal control based on deep reinforcement learning can achieve better resilience in most of the cases, particularly in the scenarios of moderate and severe  ...  Acknowledgments is work was supported in part by the Key Special Project of Beijing City (Grant no. Z181100003918011).  ... 
doi:10.1155/2020/8841317 fatcat:jlvteaivv5exzfdtg6cx4qak2q

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Huichu Zhang, Zhenhui Li, Siyuan Feng, Chang Liu, Yaoyao Ding, Yichen Zhu, Zihan Zhou, Weinan Zhang, Yong Yu, Haiming Jin
2019 The World Wide Web Conference on - WWW '19  
Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic  ...  Traffic signal control is an emerging application scenario for reinforcement learning.  ...  SUMMARY We propose CityFlow, an efficient, multi-agent reinforcement learning environment for large scale city traffic scenario.  ... 
doi:10.1145/3308558.3314139 dblp:conf/www/ZhangFLDZZ00JL19 fatcat:wmz7q67rmfdbjllkk3mm6llz2u

Urban Traffic Control Using Adjusted Reinforcement Learning in a Multi-agent System

Mahshid Helali Moghadam, Nasser Mozayani
2013 Research Journal of Applied Sciences Engineering and Technology  
This study presents an adaptive approach to control urban traffic using multi-agent systems and a reinforcement learning augmented by an adjusting pre-learning stage.  ...  Dynamism, continuous changes of states and the necessity to respond quickly are the specific characteristics of the environment in a traffic control system.  ...  URBAN TRAFFIC CONTROL BASED ON ADJUSTED REINFORCEMENT LEARNING The proposed approach for traffic signals control is based on use of a reinforcement learning which is accompanied with an adapting pre-learning  ... 
doi:10.19026/rjaset.6.3676 fatcat:gdl275musbdqlfnchqzvzpas5y

Cooperative, hybrid agent architecture for real-time traffic signal control

Min Chee Choy, D. Srinivasan, Ruey Long Cheu
2003 IEEE transactions on systems, man and cybernetics. Part A. Systems and humans  
learning, learning rate and weight adjustment as well as dynamic update of fuzzy relations using evolutionary algorithm.  ...  In order for the multiagent architecture to adapt itself continuously to the dynamically changing problem domain, a multistage online learning process for each agent is implemented involving reinforcement  ...  ACKNOWLEDGMENT The authors would like to thank the Land Transportation Authority of Singapore (LTA) for providing data necessary for the simulation modeling and F. Logi for his advice.  ... 
doi:10.1109/tsmca.2003.817394 fatcat:i7zeyho2gncrfddt5i534uheke

The Use of Reinforcement Learning Algorithms in Traffic Control of High Speed Networks [chapter]

Antonios F. Atlasis, Athanasios V. Vasilakos
2002 International Series in Intelligent Technologies  
Several approaches based on Computational Intelligence techniques that develop efficient solutions to some of the most significant traffic control problems of High Speed Networks have been proposed so  ...  Although this framework is developed using specific proposed mechanisms and Reinforcement Learning Algorithm, it can give some general but efficient guidelines that can be used irrespective of the RLA  ...  THE USE OF REINFORCEMENT LEARNING ALGORITHMS IN TRAFFIC CONTROL OF HIGH SPEED NETWORKS The need for learning in High Speed Networks depends on the information that is available regarding the network, the  ... 
doi:10.1007/978-94-010-0324-7_25 fatcat:wklab4ryjzfndbctq7s7azbvsa

Guest Editorial: AI Applications to Intelligent Vehicles for Advancing Intelligent Transport Systems

2020 IET Intelligent Transport Systems  
He serves as the reviewer from more than 20 known journals, and was awarded as an outstanding reviewer by Mechanical Systems and Signal Processing and Mechatronics in 2018.  ...  In 'Three-dimensional trajectory tracking of an underactuated AUV based on fuzzy dynamic surface control', Liang et al. study a three-dimensional curve trajectory tracking control of an underactuated autonomous  ...  In 'Distributed predictive cruise control based on reinforcement learning and validation on microscopic traffic simulation', Mnuddin and Gao propose a novel distributed predictive cruise control (PCC)  ... 
doi:10.1049/iet-its.2020.0189 fatcat:7cgsidr4lfeqvd3ffjtmv4guwi

Reinforcement Learning in Dynamic Task Scheduling: A Review

Chathurangi Shyalika, Thushari Silva, Asoka Karunananda
2020 SN Computer Science  
The paper addresses the results of the study by means of the state-of-theart on Reinforcement learning techniques used in dynamic task scheduling and a comparative review of those techniques.  ...  This review paper is about a research study that focused on Reinforcement Learning techniques that have been used for dynamic task scheduling.  ...  Compliance with ethical Standards Conflicts of Interest/Competing Interests The authors declare that there are no conflicts of interest regarding the publication of this article.  ... 
doi:10.1007/s42979-020-00326-5 fatcat:egp6vgpetbcwdasm45vunmo3n4

Evaluation and Application of Urban Traffic Signal Optimizing Control Strategy Based on Reinforcement Learning

Yizhe Wang, Xiaoguang Yang, Yangdong Liu, Hailun Liang
2018 Journal of Advanced Transportation  
In a word, the problem of insufficient traffic signal control capability is studied, and the hierarchical control algorithm based on reinforcement learning is applied to traffic signal control, so as to  ...  Based on the three key parameters of cycle, arterial coordination offset, and green split, a set of hierarchical control algorithms based on reinforcement learning is constructed to optimize and improve  ...  Acknowledgments The authors would like to acknowledge the Intelligent Transportation System Research Center of Tongji University for data support.  ... 
doi:10.1155/2018/3631489 fatcat:4sqihpd5sbfyji26vkgedz3b7q

A Review of the Self-Adaptive Traffic Signal Control System Based on Future Traffic Environment

Yizhe Wang, Xiaoguang Yang, Hailun Liang, Yangdong Liu
2018 Journal of Advanced Transportation  
Finally, the article concluded that signal control based on multiagent reinforcement learning is a kind of closed-loop feedback adaptive control method, which outperforms many counterparts in terms of  ...  real-time characteristic, accuracy, and self-learning and therefore will be an important research focus of control method in future due to the property of "model-free" and "self-learning" that well accommodates  ...  Acknowledgments The research is supported by Project of National Natural Science Foundation of China (Project no. 61773293) and Key Project of National Natural Science Foundation of China (Project no.  ... 
doi:10.1155/2018/1096123 fatcat:ynkoxgm6lnflrpliafo6xf6opy

An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control

Qiang Wu, Jianqing Wu, Jun Shen, Binbin Yong, Qingguo Zhou
2020 Sensors  
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  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20154291 pmid:32752055 pmcid:PMC7436084 fatcat:czp6qn2mpncchfco2sjohm76ny

Table of Contents

2020 2020 IEEE 45th Conference on Local Computer Networks (LCN)  
Learning-Based Dynamic Retransmission Mechanism for Mission Critical Communication: An Edge-Computing Approach 393 An Adaptive TXOP Sharing Algorithm for Multimedia Traffic in IEEE802.11ac Networks  ...  Wireless Networks 325 IQoR: An Intelligent QoS-Aware Routing Mechanism with Deep Reinforcement Learning 329 Secure and Reliable Data Transmission in SDN-Based Backend Networks of Industrial IoT Detecting  ... 
doi:10.1109/lcn48667.2020.9314824 fatcat:ijv6a3vurbd2zjmdmkt7bxle4q

Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning

Pallavi Mandhare, Dr. Jyoti Yadav, Prof. Vilas Kharat, Prof. C.Y. Patil
2021 Information Technology in Industry  
The control and coordination of traffic signal control systems can be effectively achieved by implementing the Deep Reinforcement Learning (DRL) approaches.  ...  Safe and smooth movement of traffic is ensured by a self-controlled traffic signal. As such, to coordinate the traffic flow it is necessary to implement dynamic traffic signal subsequences.  ...  Index Terms-Deep Learning, Reinforcement Learning, Simulation (Agent-based), SUMO Simulator, Traffic Signal Controller. 1 .  ... 
doi:10.17762/itii.v9i1.141 fatcat:o2bhjpadibemdfbdqbsmqybabm
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