Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways
ABSTRACTIn the last decade, agent-based modelling and simulation has emerged as a potential approach to study complex systems in the real world, such as traffic congestion. Complex systems could be modelled as a collection of autonomous agents, who observe the external environment, interact with eac...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-07-01
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| Series: | Journal of Information and Telecommunication |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/24751839.2023.2182174 |
| _version_ | 1827892039184809984 |
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| author | Nguyen-Tuan-Thanh Le |
| author_facet | Nguyen-Tuan-Thanh Le |
| author_sort | Nguyen-Tuan-Thanh Le |
| collection | DOAJ |
| description | ABSTRACTIn the last decade, agent-based modelling and simulation has emerged as a potential approach to study complex systems in the real world, such as traffic congestion. Complex systems could be modelled as a collection of autonomous agents, who observe the external environment, interact with each other and perform suitable actions. In addition, reinforcement learning, a branch of Machine Learning, that models the learning process of a single agent as a Markov decision process, has recently achieved remarkable results in several domains (e.g. Atari games, Dota 2, Go, Self-driving cars, Protein folding, etc.), especially with the invention of deep reinforcement learning. Multi-agent reinforcement learning, by taking advantage of these two approaches, is a new technique that can be used to further study complex systems. In this article, we present a multi-agent reinforcement learning model for traffic congestion on one-way multi-lane highways and experiment with six reinforcement learning algorithms in this setting. |
| first_indexed | 2024-03-12T21:34:48Z |
| format | Article |
| id | doaj.art-ba3fea3bf3ad495ca8c73ff61b4a2646 |
| institution | Directory Open Access Journal |
| issn | 2475-1839 2475-1847 |
| language | English |
| last_indexed | 2024-03-12T21:34:48Z |
| publishDate | 2023-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Information and Telecommunication |
| spelling | doaj.art-ba3fea3bf3ad495ca8c73ff61b4a26462023-07-27T11:47:07ZengTaylor & Francis GroupJournal of Information and Telecommunication2475-18392475-18472023-07-017325526910.1080/24751839.2023.2182174Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highwaysNguyen-Tuan-Thanh Le0Department of Software Engineering, Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, VietnamABSTRACTIn the last decade, agent-based modelling and simulation has emerged as a potential approach to study complex systems in the real world, such as traffic congestion. Complex systems could be modelled as a collection of autonomous agents, who observe the external environment, interact with each other and perform suitable actions. In addition, reinforcement learning, a branch of Machine Learning, that models the learning process of a single agent as a Markov decision process, has recently achieved remarkable results in several domains (e.g. Atari games, Dota 2, Go, Self-driving cars, Protein folding, etc.), especially with the invention of deep reinforcement learning. Multi-agent reinforcement learning, by taking advantage of these two approaches, is a new technique that can be used to further study complex systems. In this article, we present a multi-agent reinforcement learning model for traffic congestion on one-way multi-lane highways and experiment with six reinforcement learning algorithms in this setting.https://www.tandfonline.com/doi/10.1080/24751839.2023.2182174Multi-agent reinforcement learningReinforcement learningMulti-agent systemsTraffic congestion |
| spellingShingle | Nguyen-Tuan-Thanh Le Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways Journal of Information and Telecommunication Multi-agent reinforcement learning Reinforcement learning Multi-agent systems Traffic congestion |
| title | Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways |
| title_full | Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways |
| title_fullStr | Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways |
| title_full_unstemmed | Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways |
| title_short | Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways |
| title_sort | multi agent reinforcement learning for traffic congestion on one way multi lane highways |
| topic | Multi-agent reinforcement learning Reinforcement learning Multi-agent systems Traffic congestion |
| url | https://www.tandfonline.com/doi/10.1080/24751839.2023.2182174 |
| work_keys_str_mv | AT nguyentuanthanhle multiagentreinforcementlearningfortrafficcongestionononewaymultilanehighways |