The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing
Open accessAbstract
An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV' UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs' trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.
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Cites
Adam: A Method for Stochastic Optimization
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Continuous control with deep reinforcement learning
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A Survey on Mobile Edge Computing: The Communication Perspective
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Wireless communications with unmanned aerial vehicles: opportunities and challenges
Zeng, Y., Zhang, R., & Lim, T. J. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.7470933 · Cited by 4,048
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318
A Comprehensive Survey of Multiagent Reinforcement Learning
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Metadata
- Authors
- Wang, L., Wang, K., Pan, C., Xu, W., Aslam, N., & Hanzo, L.
- Venue
- IEEE Transactions on Cognitive Communications and Networking
- Published
- 2021-03
- Volume
- 7
- Issue
- 1
- Pages
- 73-84
- ISSN
- 2332-7731 (ISSN-L)
- 2372-2045
- OpenAlex
- W3090573937
- Cited by
- 510
- References
- 9
- FWCI
- 33.03
- Type
- journal article
- Language
- en
- Open access
- green
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- Wang, L., Wang, K., Pan, C., Xu, W., Aslam, N., & Hanzo, L. (2021). Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing. IEEE Transactions on Cognitive Communications and Networking. https://doi.org/10.1109/tccn.2020.3027695
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