Multi-Agent Deep Reinforcement Learning for Cooperative Driving in Crowded Traffic Scenarios
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jongwon | - |
dc.contributor.author | Min, Kyushik | - |
dc.contributor.author | Huh, Kunsoo | - |
dc.date.accessioned | 2021-07-30T05:22:49Z | - |
dc.date.available | 2021-07-30T05:22:49Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2019-12 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4476 | - |
dc.description.abstract | For autonomous vehicles, lane changes on crowded roads are difficult to be performed without interactions and cooperation between vehicles. This paper proposes a novel method to learn interaction and cooperate between the multiple vehicles to solve the complex traffic problem through Multi-Agent Reinforcement Learning (MARL). The proposed network is designed based on the interaction network to learn optimal control strategies considering interaction between vehicles. By applying the proposed algorithm, the network can control and train the agents regardless of the number of agents. It is a practical advantage because the number of the vehicles is constantly changed in the real environment. The proposed method is evaluated in the connected car environment where all vehicles can exchange information with each other. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Multi-Agent Deep Reinforcement Learning for Cooperative Driving in Crowded Traffic Scenarios | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Huh, Kunsoo | - |
dc.identifier.doi | 10.1109/ISPACS48206.2019.8986374 | - |
dc.identifier.scopusid | 2-s2.0-85081090307 | - |
dc.identifier.wosid | 000545533600143 | - |
dc.identifier.bibliographicCitation | 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp.1 - 2 | - |
dc.relation.isPartOf | 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) | - |
dc.citation.title | 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 2 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | Fertilizers | - |
dc.subject.keywordPlus | Multi agent systems | - |
dc.subject.keywordPlus | Reinforcement learning | - |
dc.subject.keywordPlus | Road vehicles | - |
dc.subject.keywordPlus | Signal processing | - |
dc.subject.keywordPlus | Cooperative driving | - |
dc.subject.keywordPlus | Interaction networks | - |
dc.subject.keywordPlus | Lane change | - |
dc.subject.keywordPlus | Multi agent | - |
dc.subject.keywordPlus | Multi-agent reinforcement learning | - |
dc.subject.keywordPlus | Optimal control strategy | - |
dc.subject.keywordPlus | Real environments | - |
dc.subject.keywordPlus | Traffic problems | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordAuthor | connected car | - |
dc.subject.keywordAuthor | cooperative driving | - |
dc.subject.keywordAuthor | interaction network | - |
dc.subject.keywordAuthor | multi -agent reinforcement learning | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/8986374 | - |
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