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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2300393


A multi-agent collaboration scheme for energy efficient task scheduling in 3D UAV-MEC space


Author(s):  Yang LI, Ziling WEI, Jinshu SU, Baokang ZHAO

Affiliation(s):  The College of Computer, National University of Defense Technology, Changsha 410073, China; more

Corresponding email(s):   liyang20@nudt.edu.cn, weiziling@nudt.edu.cn, sjs@nudt.edu.cn, bkzhao@nudt.edu.cn

Key Words:  Multi-access edge computing, Multi-agent reinforcement learning, Unmanned aerial vehicles, Task scheduling


Yang LI, Ziling WEI, Jinshu SU, Baokang ZHAO. A multi-agent collaboration scheme for energy efficient task scheduling in 3D UAV-MEC space[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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Abstract: 
multi-access edge computing (MEC) presents computing services at the edge of networks to address the enormous processing requirements of intelligent applications. Due to the maneuverability of unmanned aerial vehicles (UAVs), they can be utilized as temporal aerial edge nodes for providing edge services to ground users in MEC. However, the MEC environment is usually dynamic and complicated. It is a challenge for multiple UAVs to select appropriate service strategies. Besides, most of the existing works study UAV-MEC with the assumption that the flight heights of UAVs are fixed, i.e., the flying is considered to occur with reference to a two-dimensional (2D) plane, which neglects the importance of the height. In this paper, with consideration of the co-channel interference, an optimization problem of energy efficiency is investigated to maximize the amount of fulfilled tasks, where multiple UAVs in three-dimensional (3D) space collaboratively fulfill the task’s computation of ground users. In the formulated problem, we try to obtain the optimal flight and sub-channel selection strategies for UAVs and schedule strategies for tasks. Based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, we propose a Curiosity-driven and Twin-networks-structured MADDPG (CTMADDPG) algorithm to solve the formulated problem. It utilizes the inner reward to facilitate the state exploration of agents, avoiding convergence at the sub-optimal strategy. Furthermore, we adopt the twin critic networks for update stabilization to reduce the probability of Q value overestimation. The simulation results show that CTMADDPG is outstanding in maximizing the energy efficiency of the whole system. Abundant experiments show that the proposed scheme outperforms other benchmarks.

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