Access the full text.
Sign up today, get DeepDyve free for 14 days.
J.S. Sussman (2008)
Perspectives on intelligent transportation systems (ITS)
Tim Brys, A. Harutyunyan, Peter Vrancx, A. Nowé, Matthew Taylor (2017)
Multi-objectivization and ensembles of shapings in reinforcement learningNeurocomputing, 263
Hua Wei, Guanjie Zheng, Huaxiu Yao, Z. Li (2018)
IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light ControlProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Stephen Ezell (2010)
Explaining International IT Application Leadership: Intelligent Transportation Systems
Longxin Lin (1992)
Reinforcement learning for robots using neural networks
Seung-Bae Cools, C. Gershenson, Bart D'Hooghe (2006)
Self-organizing traffic lights: A realistic simulation
M. Alam, J. Ferreira, J. Fonseca (2016)
Intelligent transportation systems
S.B. Cools, C. Gershenson, B. D'Hooghe (2013)
Advances in applied self?organizing systems
H. Seijen, Mehdi Fatemi, R. Laroche, Joshua Romoff, Tavian Barnes, Jeffrey Tsang (2017)
Hybrid Reward Architecture for Reinforcement LearningArXiv, abs/1706.04208
Sakhawat Hossan, Naushin Nower (2020)
Fog-based dynamic traffic light control system for improving public transportPublic Transport, 12
I. Porche, S. Lafortune (1999)
Adaptive Look-ahead Optimization of Traffic SignalsJ. Intell. Transp. Syst., 4
Oscar Egu, P. Bonnel (2020)
Can we estimate accurately fare evasion without a survey? Results from a data comparison approach in Lyon using fare collection data, fare inspection data and counting dataPublic Transport, 12
E. Van der Pol, F.A. Oliehoek (2016)
Coordinated deep reinforcement learners for traffic light control
M. Van Otterlo, M. Wiering (2012)
Reinforcement learning
Tim Brys, T. Pham, Matthew Taylor (2014)
Distributed learning and multi-objectivity in traffic light controlConnection Science, 26
K. Dresner, P. Stone (2005)
Multiagent Traffic Management: Opportunities for Multiagent Learning
Li Li, Yisheng Lv, Feiyue Wang (2016)
Traffic signal timing via deep reinforcement learningIEEE/CAA Journal of Automatica Sinica, 3
Yoshina Takano, Wenwen Ouyang, Suguru Ito, Tomohiro Harada, R. Thawonmas (2018)
Applying Hybrid Reward Architecture to a Fighting Game AI2018 IEEE Conference on Computational Intelligence and Games (CIG)
B. Abdulhai, R. Pringle, G. Karakoulas (2003)
Reinforcement learning for true adaptive traffic signal controlJournal of Transportation Engineering-asce, 129
F.V. Webster (1958)
Traffic signal settings
Daniel Krajzewicz, J. Erdmann, M. Behrisch, Laura Bieker (2012)
Recent Development and Applications of SUMO - Simulation of Urban MObility, 5
Intelligent transportation systems
P. Mannion, J. Duggan, E. Howley (2016)
An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control
Michel Tokic, G. Palm (2011)
Value-Difference Based Exploration: Adaptive Control between Epsilon-Greedy and Softmax
Ming Tan (1997)
Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents
M. Wiering (2000)
Multi-Agent Reinforcement Leraning for Traffic Light Control
Wade Genders, S. Razavi (2016)
Using a Deep Reinforcement Learning Agent for Traffic Signal ControlArXiv, abs/1611.01142
L. Buşoniu, Robert Babuška, B. Schutter (2008)
A Comprehensive Survey of Multiagent Reinforcement LearningIEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38
Tim Brys, A. Harutyunyan, Peter Vrancx, Matthew Taylor, D. Kudenko, A. Nowé (2014)
Multi-objectivization of reinforcement learning problems by reward shaping2014 International Joint Conference on Neural Networks (IJCNN)
Matthew Taylor, Manish Jain, Prateek Tandon, M. Yokoo, Milind Tambe (2011)
Distributed on-Line Multi-Agent Optimization under Uncertainty: Balancing Exploration and ExploitationAdv. Complex Syst., 14
F. Dion, Hesham Rakha, Youn-Soo Kang (2004)
COMPARISON OF DELAY ESTIMATES AT UNDER-SATURATED AND OVER-SATURATED PRE-TIMED SIGNALIZED INTERSECTIONSTransportation Research Part B-methodological, 38
M. Otterlo, M. Wiering (2012)
Reinforcement Learning and Markov Decision Processes
Hua Wei, Nan Xu, Huichu Zhang, Guanjie Zheng, Xinshi Zang, Chacha Chen, Weinan Zhang, Yanmin Zhu, Kai Xu, Z. Li (2019)
CoLight: Learning Network-level Cooperation for Traffic Signal ControlProceedings of the 28th ACM International Conference on Information and Knowledge Management
Alan Miller (1963)
Settings for Fixed-Cycle Traffic SignalsJournal of the Operational Research Society, 14
K. Gurney (1997)
An introduction to neural networks
, 9
P. Mannion, J. Duggan, E. Howley (2016)
Autonomic road transport support systems
P. Balaji, X. German, D. Srinivasan (2010)
Urban traffic signal control using reinforcement learning agentsIet Intelligent Transport Systems, 4
Tomoki Nishi, Keisuke Otaki, K. Hayakawa, Takayoshi Yoshimura (2018)
Traffic Signal Control Based on Reinforcement Learning with Graph Convolutional Neural Nets2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Yilun Lin, Xingyuan Dai, Li Li, Feiyue Wang (2018)
An Efficient Deep Reinforcement Learning Model for Urban Traffic ControlArXiv, abs/1808.01876
Mustafa Coşkun, Abdelkader Baggag, S. Chawla (2018)
Deep Reinforcement Learning for Traffic Light Optimization2018 IEEE International Conference on Data Mining Workshops (ICDMW)
Bruno Silva, Eduardo Basso, F. Perotto, A. Bazzan, P. Engel (2006)
Improving reinforcement learning with context detection
Chunming Liu, Xin Xu, D. Hu (2015)
Multiobjective Reinforcement Learning: A Comprehensive OverviewIEEE Transactions on Systems, Man, and Cybernetics: Systems, 45
I. Goodfellow, Y. Bengio, A. Courville (2016)
Deep learning
Seyed Mousavi, M. Schukat, P. Corcoran, E. Howley (2017)
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement LearningArXiv, abs/1704.08883
L. Trafficware (2017)
Synchro studio 10
A.R.M. Jamil, K.K. Ganguly, N. Nower (2020)
An experimental analysis of reward functions for adaptive traffic signal control system
C. Claus, Craig Boutilier (1998)
The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems
A. Joseph, A. Beresford, J. Bacon, D. Cottingham, J. Davies, Brian Jones, Haitao Guo, Wei Guan, Yong Lin, Houbing Song, L. Iftode, Simone Fuchs, B. Lamprecht, K. Kyamakya, Jorge Fernández, Juan García, Y. García, Jorge Santos, Milind Nimesh, Gang Pan, Zhaohui Wu, Qing Wu, Zhenyu Shan, Jie Sun, Jian Lu, Guoqing Yang, M. Khan, Jiashu Zhang
Transportation Engineering and Planning – Vol. Ii -intelligent Transportation Systems Intelligent Transportation Systems
M. Weinberg, J.S. Rosenschein (2004)
Best?response multiagent learning in nonstationary environments
Juntao Gao, Yulong Shen, Jia Liu, Minoru Ito, N. Shiratori (2017)
Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target NetworkArXiv, abs/1705.02755
I. Arel, C. Liu, T. Urbanik, A. Kohls (2010)
Reinforcement learning-based multi-agent system for network traffic signal controlIet Intelligent Transport Systems, 4
Matthew Zeiler (2012)
ADADELTA: An Adaptive Learning Rate MethodArXiv, abs/1212.5701
R.S. Sutton, A.G. Barto (1998)
Introduction to reinforcement learning, 135
Y. Liu, Lei Liu, Wei-Peng Chen (2017)
Intelligent traffic light control using distributed multi-agent Q learning2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)
Samah El-Tantawy, B. Abdulhai, H. Abdelgawad (2013)
Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown TorontoIEEE Transactions on Intelligent Transportation Systems, 14
IET Intelligent Transport Systems – Wiley
Published: Dec 1, 2020
Keywords: ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
You can share this free article with as many people as you like with the url below! We hope you enjoy this feature!
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.