Deep Reinforcement Learning with Local Attention for Single Agile Optical Satellite Scheduling Problem
This paper investigates the single agile optical satellite scheduling problem, which has received increasing attention due to the rapid growth in earth observation requirements. Owing to the complicated constraints and considerable solution space of this problem, the conventional exact methods and h...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/19/6396 |
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| author | Zheng Liu Wei Xiong Chi Han Xiaolan Yu |
| author_facet | Zheng Liu Wei Xiong Chi Han Xiaolan Yu |
| author_sort | Zheng Liu |
| collection | DOAJ |
| description | This paper investigates the single agile optical satellite scheduling problem, which has received increasing attention due to the rapid growth in earth observation requirements. Owing to the complicated constraints and considerable solution space of this problem, the conventional exact methods and heuristic methods, which are sensitive to the problem scale, demand high computational expenses. Thus, an efficient approach is demanded to solve this problem, and this paper proposes a deep reinforcement learning algorithm with a local attention mechanism. A mathematical model is first established to describe this problem, which considers a series of complex constraints and takes the profit ratio of completed tasks as the optimization objective. Then, a neural network framework with an encoder–decoder structure is adopted to generate high-quality solutions, and a local attention mechanism is designed to improve the generation of solutions. In addition, an adaptive learning rate strategy is proposed to guide the actor–critic training algorithm to dynamically adjust the learning rate in the training process to enhance the training effectiveness of the proposed network. Finally, extensive experiments verify that the proposed algorithm outperforms the comparison algorithms in terms of solution quality, generalization performance, and computation efficiency. |
| format | Article |
| id | doaj-art-3da67f0ed0f843f397c28adac85bb11e |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3da67f0ed0f843f397c28adac85bb11e2025-08-20T01:47:37ZengMDPI AGSensors1424-82202024-10-012419639610.3390/s24196396Deep Reinforcement Learning with Local Attention for Single Agile Optical Satellite Scheduling ProblemZheng Liu0Wei Xiong1Chi Han2Xiaolan Yu3National Key Laboratory of Space Target Awareness, Space Engineering University, Beijing 101416, ChinaNational Key Laboratory of Space Target Awareness, Space Engineering University, Beijing 101416, ChinaNational Key Laboratory of Space Target Awareness, Space Engineering University, Beijing 101416, ChinaNational Key Laboratory of Space Target Awareness, Space Engineering University, Beijing 101416, ChinaThis paper investigates the single agile optical satellite scheduling problem, which has received increasing attention due to the rapid growth in earth observation requirements. Owing to the complicated constraints and considerable solution space of this problem, the conventional exact methods and heuristic methods, which are sensitive to the problem scale, demand high computational expenses. Thus, an efficient approach is demanded to solve this problem, and this paper proposes a deep reinforcement learning algorithm with a local attention mechanism. A mathematical model is first established to describe this problem, which considers a series of complex constraints and takes the profit ratio of completed tasks as the optimization objective. Then, a neural network framework with an encoder–decoder structure is adopted to generate high-quality solutions, and a local attention mechanism is designed to improve the generation of solutions. In addition, an adaptive learning rate strategy is proposed to guide the actor–critic training algorithm to dynamically adjust the learning rate in the training process to enhance the training effectiveness of the proposed network. Finally, extensive experiments verify that the proposed algorithm outperforms the comparison algorithms in terms of solution quality, generalization performance, and computation efficiency.https://www.mdpi.com/1424-8220/24/19/6396single agile optical satellite schedulingdeep reinforcement learninglocal attentionadaptive learning rate |
| spellingShingle | Zheng Liu Wei Xiong Chi Han Xiaolan Yu Deep Reinforcement Learning with Local Attention for Single Agile Optical Satellite Scheduling Problem Sensors single agile optical satellite scheduling deep reinforcement learning local attention adaptive learning rate |
| title | Deep Reinforcement Learning with Local Attention for Single Agile Optical Satellite Scheduling Problem |
| title_full | Deep Reinforcement Learning with Local Attention for Single Agile Optical Satellite Scheduling Problem |
| title_fullStr | Deep Reinforcement Learning with Local Attention for Single Agile Optical Satellite Scheduling Problem |
| title_full_unstemmed | Deep Reinforcement Learning with Local Attention for Single Agile Optical Satellite Scheduling Problem |
| title_short | Deep Reinforcement Learning with Local Attention for Single Agile Optical Satellite Scheduling Problem |
| title_sort | deep reinforcement learning with local attention for single agile optical satellite scheduling problem |
| topic | single agile optical satellite scheduling deep reinforcement learning local attention adaptive learning rate |
| url | https://www.mdpi.com/1424-8220/24/19/6396 |
| work_keys_str_mv | AT zhengliu deepreinforcementlearningwithlocalattentionforsingleagileopticalsatelliteschedulingproblem AT weixiong deepreinforcementlearningwithlocalattentionforsingleagileopticalsatelliteschedulingproblem AT chihan deepreinforcementlearningwithlocalattentionforsingleagileopticalsatelliteschedulingproblem AT xiaolanyu deepreinforcementlearningwithlocalattentionforsingleagileopticalsatelliteschedulingproblem |