A distributed nash Q-learning approach for optimizing urban traffic

Journal of Convergence Information Technology

Volumn 7, Issue 2, 2012, Pages 92-100

  Xia, Xin Hai ^a,b   Xu, Lun Hui ^a   Kuang, Xian Yan ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b Guangzhou Marine College   (China)

Author keywords

Agent; Learning; Markov game; Traffic control

Indexed keywords

AGENT CONTROL; HIGH GROWTH RATE; INTERACTION MODEL; JOINT ACTIONS; LEARNING; MARKOV GAMES; MULTI-AGENT REINFORCEMENT LEARNING; NASH-Q LEARNING; OPTIMAL ACTIONS; PER CAPITA; Q-LEARNING; Q-VALUES; SIGNALIZED INTERSECTION; SINGLE-AGENT; TRAFFIC LIGHT; TRAFFIC SIGNAL CONTROL; URBAN TRAFFIC; URBAN TRAFFIC CONTROL;

AGENTS; OPTIMIZATION; STREET TRAFFIC CONTROL; TRAFFIC CONTROL; TRAFFIC SIGNALS; TRAFFIC SIGNS;

MULTI AGENT SYSTEMS;

EID: 84863180555     PISSN: 19759320     EISSN: 22339299     Source Type: Journal    
DOI: 10.4156/jcit.vol7.issue2.11     Document Type: Article

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