Lightweight deep reinforcement learning for dynamic resource allocation in vehicular edge computing Article Swipe
Dapeng Wu
,
Sijun Wu
,
Yaping Cui
,
Ailing Zhong
,
Tong Tang
,
Ruyan Wang
,
Xinqi Lin
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.dcan.2025.06.005
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.dcan.2025.06.005
Vehicular Edge Computing (VEC) enhances the quality of user services by deploying wealth of resources near vehicles. However, due to highly dynamic and complex nature of vehicular networks, centralized decision-making for resource allocation proves inadequate within VECs. Conversely, allocating resources via distributed decision-making consumes vehicular resources. To improve the quality of user service, we formulate a problem of latency minimization, further subdividing this problem into two subproblems to be solved through distributed decision-making. To mitigate the resource consumption caused by distributed decision-making, we propose Reinforcement Learning (RL) algorithm based on sequential alternating multi-agent system mechanism, which effectively reduces the dimensionality of action space without losing the informational content of action, achieving network lightweighting. We discuss the rationality, generalizability, and inherent advantages of proposed mechanism. Simulation results indicate that our proposed mechanism outperforms traditional RL algorithms in terms of stability, generalizability, and adaptability to scenarios with invalid actions, all while achieving network lightweighting.
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- article
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- Landing Page
- https://doi.org/10.1016/j.dcan.2025.06.005
- OA Status
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- References
- 26
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https://openalex.org/W4411371109Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.dcan.2025.06.005Digital Object Identifier
- Title
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Lightweight deep reinforcement learning for dynamic resource allocation in vehicular edge computingWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
- Publication date
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2025-06-17Full publication date if available
- Authors
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Dapeng Wu, Sijun Wu, Yaping Cui, Ailing Zhong, Tong Tang, Ruyan Wang, Xinqi LinList of authors in order
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https://doi.org/10.1016/j.dcan.2025.06.005Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1016/j.dcan.2025.06.005Direct OA link when available
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Computer science, Reinforcement learning, Enhanced Data Rates for GSM Evolution, Edge computing, Resource allocation, Artificial intelligence, Distributed computing, Computer networkTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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26Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.dimensionality | 99 |
| abstract_inverted_index.decision-making | 29, 42 |
| abstract_inverted_index.lightweighting. | 112, 151 |
| abstract_inverted_index.decision-making, | 81 |
| abstract_inverted_index.decision-making. | 72 |
| abstract_inverted_index.generalizability, | 117, 139 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.26092101 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |