Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

2020-01-24 · Feibo Jiang, Kezhi Wang, Li Dong, Cunhua Pan, Kun Yang

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet of things (IoT) users, by optimizing offloading decision, transmission power and resource allocation in the large-scale mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) based solution is proposed, which includes the following components. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks.

📄 PDF Abstract BibTeX arXiv:2001.09223

Code (0)

등록된 구현이 없습니다.

Tasks

Data CompressionDeep Reinforcement LearningEdge-computingReinforcement LearningScheduling

Methods 이 논문이 사용한 방법론

Prioritized Experience Replay Prioritized Experience Replay is a type of experience replay in reinforcement learning where we more frequently replay…

Experience Replay Experience Replay is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e\_{t} = \left(s\_{t}, a\_{t}, r\_{t},…

Remote sensing framework for geological mapping via stacked autoencoders and clustering

2024-04-02 · Sandeep Nagar, Ehsan Farahbakhsh, Joseph Awange, Rohitash Chandra

Supervised machine learning methods for geological mapping via remote sensing face limitations due to the scarcity of accurately labelled training data that can be addressed by unsupervised learning, such as dimensionali…

Clustering Dimensionality Reduction

Stacked Autoencoders for Medical Image Search

2016-10-02 · S. Sharma, I. Umar, L. Ospina, D. Wong 외

Medical images can be a valuable resource for reliable information to support medical diagnosis. However, the large volume of medical images makes it challenging to retrieve relevant information given a particular scenar…

Content-Based Image Retrieval Image Retrieval Medical Diagnosis Retrieval

MO-SAE:Multi-Objective Stacked Autoencoders Optimization for Edge Anomaly Detection

2026-03-14 · Lizhao Zhang, Shengsong Kong, Tao Guo, Shaobo Li 외 arxiv

Stacked AutoEncoders (SAE) have been widely adopted in edge anomaly detection scenarios. However, the resource-intensive nature of SAE can pose significant challenges for edge devices, which are typically resource-constr…

Anomaly Detection

Online Adaptation of Deep Architectures with Reinforcement Learning

2016-08-08 · Thushan Ganegedara, Lionel Ott, Fabio Ramos

Online learning has become crucial to many problems in machine learning. As more data is collected sequentially, quickly adapting to changes in the data distribution can offer several competitive advantages such as avoid…

Denoising reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder

2015-03-22 · Angshul Majumdar

In this work we address the problem of real-time dynamic MRI reconstruction. There are a handful of studies on this topic; these techniques are either based on compressed sensing or employ Kalman Filtering. These techniq…

compressed sensing Denoising MRI Reconstruction