MULTIAGENT-BASED TRAFFIC SIGNAL CONTROL WITH REINFORCEMENT LEARNING
In this paper, the authors present a multi-agent system which adapts itself to a restricted environment such as traffic signal control. The signals are treated as agents, and as they autonomously act and learn, they adjust to the change of traffic volume. Simulation results show the effectiveness of this system.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/21259390
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Supplemental Notes:
- Publication Date: May 2000
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Corporate Authors:
Kanazawa Daigaku
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Authors:
- Misawa, T
- Kimura, H
- Hinose, S
- Osato, N
- Publication Date: 2000
Language
- Japanese
Media Info
- Pagination: p. 478-486
- Serial:
Subject/Index Terms
- TRT Terms: Artificial intelligence; Traffic control; Traffic signals
- Subject Areas: Operations and Traffic Management;
Filing Info
- Accession Number: 00800014
- Record Type: Publication
- Source Agency: UC Berkeley Transportation Library
- Files: PATH
- Created Date: Oct 12 2000 12:00AM