Distributed multi-agent deep reinforcement learning for trajectory planning in UAVs-assisted edge offloading
Author:
Funder
National Natural Science Foundation of China
Jiangsu Natural Science Foundation
Anhui Provincial Key Research and Development Project
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42486-024-00159-8.pdf
Reference36 articles.
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2. Cai, T., Yang, Z., Chen, Y., Chen, W., Zheng, Z., Yu, Y., Dai, H.-N.: Cooperative data sensing and computation offloading in uav-assisted crowdsensing with multi-agent deep reinforcement learning. IEEE Trans. Netw. Sci. Eng. 9(5), 3197–3211 (2021)
3. Cheng, Z., Gao, Z., Liwang, M., Huang, L., Du, X., Guizani, M.: Intelligent task offloading and energy allocation in the uav-aided mobile edge-cloud continuum. IEEE Netw. 35(5), 42–49 (2021)
4. Gao, A., Wang, Q., Liang, W., Ding, Z.: Game combined multi-agent reinforcement learning approach for uav assisted offloading. IEEE Trans. Veh. Technol. 70(12), 12888–12901 (2021)
5. Han, H., Zhan, C., Lv, J., Xu, C.: Energy minimization for cellular connected aerial edge computing system with binary offloading. IEEE Internet Things J. (2023). https://doi.org/10.1109/JIOT.2023.3323289
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