Reinforcement Learning for Joint Control of Traffic Signals in a Transportation Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jincheol | - |
dc.contributor.author | Chung, Jiyong | - |
dc.contributor.author | Sohn, Keemin | - |
dc.date.available | 2020-04-17T02:20:34Z | - |
dc.date.issued | 2020-02 | - |
dc.identifier.issn | 0018-9545 | - |
dc.identifier.issn | 1939-9359 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/38551 | - |
dc.description.abstract | Reinforcement learning (RL) approaches have recently been spotlighted for use in adaptive traffic-signal control on an area-wide level. Most researchers have employed multi-agent reinforcement learning (MARL) algorithms wherein each agent shares a holistic traffic state and cooperates with other agents to reach a common goal. However, MARL algorithms cannot guarantee a global optimal solution unless the actions of all agents are fully coordinated. The present study employs a RL algorithm that recognizes an entire traffic state and jointly controls all the traffic signals of multiple intersections. With this approach, a deep Q-network (DQN) that depends solely on traffic images is extended to overcome the curse of dimensionality that is associated with a large state and action space. Several front layers in a deep convolutional neural network (CNN) to approximate the true Q-function are shared by each intersection approach. Weight parameters connecting the last hidden layer to the output layer are fixed. The proposed methodology outperforms a fixed-signal operation, a fully actuated signal operation, a multi-agent RL control without coordination, and a multi-agent RL control with partial coordination. © 1967-2012 IEEE. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Reinforcement Learning for Joint Control of Traffic Signals in a Transportation Network | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TVT.2019.2962514 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Vehicular Technology, v.69, no.2, pp 1375 - 1387 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000519957800019 | - |
dc.identifier.scopusid | 2-s2.0-85079793298 | - |
dc.citation.endPage | 1387 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 1375 | - |
dc.citation.title | IEEE Transactions on Vehicular Technology | - |
dc.citation.volume | 69 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Adaptive traffic signal control | - |
dc.subject.keywordAuthor | Deep Q-network | - |
dc.subject.keywordAuthor | Reinforcement learning | - |
dc.subject.keywordPlus | Adaptive control systems | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.subject.keywordPlus | Deep neural networks | - |
dc.subject.keywordPlus | Fertilizers | - |
dc.subject.keywordPlus | Multi agent systems | - |
dc.subject.keywordPlus | Multilayer neural networks | - |
dc.subject.keywordPlus | Reinforcement learning | - |
dc.subject.keywordPlus | Software agents | - |
dc.subject.keywordPlus | Street traffic control | - |
dc.subject.keywordPlus | Actuated signals | - |
dc.subject.keywordPlus | Adaptive traffic signal control | - |
dc.subject.keywordPlus | Curse of dimensionality | - |
dc.subject.keywordPlus | Global optimal solutions | - |
dc.subject.keywordPlus | Multi-agent reinforcement learning | - |
dc.subject.keywordPlus | Traffic images | - |
dc.subject.keywordPlus | Transportation network | - |
dc.subject.keywordPlus | Weight parameters | - |
dc.subject.keywordPlus | Traffic signals | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalResearchArea | Transportation | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Transportation Science & Technology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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