@inproceedings{4755343913174b39b01bab7bf828b1a3,
title = "Transformer-Assisted Deep Reinforcement Learning for Distributed Latency-Sensitive Task Offloading in Mobile Edge Computing",
abstract = "In this paper, we put forward a distributed, Transformer-assisted deep reinforcement learning scheme for latency-sensitive, mobility-aware and queue-aware task offloading in mobile edge computing systems. The proposed scheme adopts an attention-based transformer and deep reinforcement learning for minimizing the average cost and the task processing latency. Since the proposed scheme is distributed, there is no single point of failure in the system. Simulation results show that the proposed scheme could significantly outperform a number of baseline schemes in the literature.",
keywords = "attention-based transformer, deep reinforcement learning, dis-tributed algorithm, latency, Mobile edge computing, queueing states, task offloading",
author = "Li, \{Yu Sheng\} and Gau, \{Rung Hung\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 59th Annual IEEE International Conference on Communications, ICC 2024 ; Conference date: 09-06-2024 Through 13-06-2024",
year = "2024",
doi = "10.1109/ICC51166.2024.10622508",
language = "English",
series = "IEEE International Conference on Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2944--2949",
editor = "Matthew Valenti and David Reed and Melissa Torres",
booktitle = "ICC 2024 - IEEE International Conference on Communications",
address = "美國",
}