Reinforcement learning-based multi-agent system for network traffic signal control

IET Intelligent Transport Systems

Volumn 4, Issue 2, 2010, Pages 128-135

  Arel, I ^a   Liu, C ^a   Urbanik, T ^a   Kohls, A G ^a  

^a University of Tennessee   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE SYSTEMS; AUTONOMOUS INTELLIGENT AGENTS; AVERAGE DELAY; CENTRAL AGENT; INTERSECTION CONTROL; MULTI-AGENT; NETWORK TRAFFIC; NOVEL METHODOLOGY; Q-LEARNING ALGORITHMS; TRAFFIC CONDITIONS; TRAFFIC NETWORKS; TRAFFIC SIGNAL CONTROL; TRAFFIC STATISTICS; VALUE FUNCTION APPROXIMATION; VALUE FUNCTIONS; VEHICULAR NETWORKS;

APPROXIMATION ALGORITHMS; FEEDFORWARD NEURAL NETWORKS; LEARNING ALGORITHMS; MULTI AGENT SYSTEMS; REINFORCEMENT; REINFORCEMENT LEARNING; TRAFFIC CONGESTION; TRAFFIC SURVEYS;

TRAFFIC SIGNALS;

EID: 77953015097     PISSN: 1751956X     EISSN: None     Source Type: Journal    
DOI: 10.1049/iet-its.2009.0070     Document Type: Article

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