MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks Monitoring
Video monitoring has a wide range of applications in a variety of scenarios, especially in smart parks. How to improve the efficiency of video data processing and reduce resource consumption have become of increasing concern. The high complexity of traditional computation offloading algorithms makes...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/23/4616 |
| _version_ | 1797462727576256512 |
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| author | Liang She Jianyuan Wang Yifan Bo Yangyan Zeng |
| author_facet | Liang She Jianyuan Wang Yifan Bo Yangyan Zeng |
| author_sort | Liang She |
| collection | DOAJ |
| description | Video monitoring has a wide range of applications in a variety of scenarios, especially in smart parks. How to improve the efficiency of video data processing and reduce resource consumption have become of increasing concern. The high complexity of traditional computation offloading algorithms makes it difficult to apply them to real-time decision-making scenarios. Thus, we propose a multi-agent deep reinforcement learning algorithm with credit assignment (MACA) for computation offloading in smart park monitoring. By making online decisions after offline training, the agent can give consideration to both decision time and accuracy in effectively solving the problem of the curse of dimensionality. Via simulation, we compare the performance of MACA with traditional deep Q-network reinforcement learning algorithm and other methods. Our results show that MACA performs better in scenarios where there are a higher number of agents and can minimize request delay and reduce task energy consumption. In addition, we also provide results from a generalization capability verified experiment and ablation study, which demonstrate the contribution of MACA algorithm to each component. |
| first_indexed | 2024-03-09T17:40:46Z |
| format | Article |
| id | doaj.art-7d9eab955f0b4116b755de5af1bad991 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T17:40:46Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-7d9eab955f0b4116b755de5af1bad9912023-11-24T11:36:35ZengMDPI AGMathematics2227-73902022-12-011023461610.3390/math10234616MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks MonitoringLiang She0Jianyuan Wang1Yifan Bo2Yangyan Zeng3School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaSchool of Computer Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, ChinaVideo monitoring has a wide range of applications in a variety of scenarios, especially in smart parks. How to improve the efficiency of video data processing and reduce resource consumption have become of increasing concern. The high complexity of traditional computation offloading algorithms makes it difficult to apply them to real-time decision-making scenarios. Thus, we propose a multi-agent deep reinforcement learning algorithm with credit assignment (MACA) for computation offloading in smart park monitoring. By making online decisions after offline training, the agent can give consideration to both decision time and accuracy in effectively solving the problem of the curse of dimensionality. Via simulation, we compare the performance of MACA with traditional deep Q-network reinforcement learning algorithm and other methods. Our results show that MACA performs better in scenarios where there are a higher number of agents and can minimize request delay and reduce task energy consumption. In addition, we also provide results from a generalization capability verified experiment and ablation study, which demonstrate the contribution of MACA algorithm to each component.https://www.mdpi.com/2227-7390/10/23/4616computation offloadingdeep reinforcement learningcredit assignmentmulti-agentvideo monitoring |
| spellingShingle | Liang She Jianyuan Wang Yifan Bo Yangyan Zeng MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks Monitoring Mathematics computation offloading deep reinforcement learning credit assignment multi-agent video monitoring |
| title | MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks Monitoring |
| title_full | MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks Monitoring |
| title_fullStr | MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks Monitoring |
| title_full_unstemmed | MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks Monitoring |
| title_short | MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks Monitoring |
| title_sort | maca multi agent with credit assignment for computation offloading in smart parks monitoring |
| topic | computation offloading deep reinforcement learning credit assignment multi-agent video monitoring |
| url | https://www.mdpi.com/2227-7390/10/23/4616 |
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