Graph Attention-based Decentralized Actor-Critic for Dual-Objective Control of Multi-UAV Swarms
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918054701563904 |
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| author | Peng, Haoran Zhang, Ying-Jun Angela |
| author_facet | Peng, Haoran Zhang, Ying-Jun Angela |
| contents | This research focuses on optimizing multi-UAV systems with dual objectives: maximizing service coverage as the primary goal while extending battery lifetime as the secondary objective. We propose a Graph Attention-based Decentralized Actor-Critic (GADC) to optimize the dual objectives. The proposed approach leverages a graph attention network to process UAVs' limited local observation and reduce the dimension of the environment states. Subsequently, an actor-double-critic network is developed to manage dual policies for joint objective optimization. The proposed GADC uses a Kullback-Leibler (KL) divergence factor to balance the tradeoff between coverage performance and battery lifetime in the multi-UAV system. We assess the scalability and efficiency of GADC through comprehensive benchmarking against state-of-the-art methods, considering both theory and experimental aspects. Extensive testing in both ideal settings and NVIDIA Sionna's realistic ray tracing environment demonstrates GADC's superior performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09195 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Attention-based Decentralized Actor-Critic for Dual-Objective Control of Multi-UAV Swarms Peng, Haoran Zhang, Ying-Jun Angela Signal Processing Artificial Intelligence Multiagent Systems This research focuses on optimizing multi-UAV systems with dual objectives: maximizing service coverage as the primary goal while extending battery lifetime as the secondary objective. We propose a Graph Attention-based Decentralized Actor-Critic (GADC) to optimize the dual objectives. The proposed approach leverages a graph attention network to process UAVs' limited local observation and reduce the dimension of the environment states. Subsequently, an actor-double-critic network is developed to manage dual policies for joint objective optimization. The proposed GADC uses a Kullback-Leibler (KL) divergence factor to balance the tradeoff between coverage performance and battery lifetime in the multi-UAV system. We assess the scalability and efficiency of GADC through comprehensive benchmarking against state-of-the-art methods, considering both theory and experimental aspects. Extensive testing in both ideal settings and NVIDIA Sionna's realistic ray tracing environment demonstrates GADC's superior performance. |
| title | Graph Attention-based Decentralized Actor-Critic for Dual-Objective Control of Multi-UAV Swarms |
| topic | Signal Processing Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2506.09195 |