We consider a mobile edge computing system that every user has multiple tasks being offloaded to edge server via wireless networks. Our goal is to acquire a satisfactory task offloading and resource allocation decision for each user so as to minimize energy consumption and delay. In this paper, we propose a deep reinforcement learning-based approach to solve joint task offloading and resource allocation problems. Simulation results show that the proposed deep Q-learning-based algorithm can achieve near-optimal performance.
CITATION STYLE
Huang, L., Feng, X., Qian, L., & Wu, Y. (2018). Deep reinforcement learning-based task offloading and resource allocation for mobile edge computing. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 251, pp. 33–42). Springer Verlag. https://doi.org/10.1007/978-3-030-00557-3_4
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