hd818's Stars
LorisMarini/content_caching_with_reinforcement_learning
Code used to simulate the results published in IEEE Xplore paper title: Distributed Caching based on Decentralized Learning Automata
davidtw0320/Resources-Allocation-in-The-Edge-Computing-Environment-Using-Reinforcement-Learning
Simulated the scenario between edge servers and users with a clear graphic interface. Also, implemented the continuous control with Deep Deterministic Policy Gradient (DDPG) to determine the resources allocation (offload targets, computational resources, migration bandwidth) in the edge servers
jiyolla/CSI4101-software-capstone-design
Network aware load balancing for edge/cloud computing using deep reinforcement learning
jordan8409212/RL-for-binary-computation-offloading-in-wireless-powered-MEC-networks
It's a implementation about the paper Liang Huang, Suzhi Bi, and Ying-jun Angela Zhang, "Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks", on https://ieeexplore.ieee.org/document/8771176
XiaTiancong/Deep-Reinforcement-Learning-for-IoT-Network-Dynamic-Clustering-in-Edge-Computing
revenol/LyDROO
Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks
revenol/DROO
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
subhanjan02/Resource-Allocation-Module
Implementation of PSO, GA, DE, PSO_TVAC, APSO
emylincon/caching_project
implementation of cooperative caching algorithm for edge computing
gargrohin/Edge-Caching-Algorithms
Edge caching algorithms for dense small cell networks. Research Project under NYU
czy36mengfei/tensorflow2_tutorials_chinese
tensorflow2中文教程,持续更新(当前版本:tensorflow2.0),tag: tensorflow 2.0 tutorials
MorvanZhou/Reinforcement-learning-with-tensorflow
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
peihaowang/DRLCache
Deep reinforcement learning-based replacement strategy for content caching.
wyc941012/Edge-Intelligence
随着移动云计算和边缘计算的快速发展,以及人工智能的广泛应用,产生了边缘智能(Edge Intelligence)的概念。深度神经网络(例如CNN)已被广泛应用于移动智能应用程序中,但是移动设备有限的存储和计算资源无法满足深度神经网络计算的需求。神经网络压缩与加速技术可以加速神经网络的计算,例如剪枝、量化、卷积核分解等。但是这些技术在实际应用非常复杂,并且可能导致模型精度的下降。在移动云计算或边缘计算中,任务卸载技术可以突破移动终端的资源限制,减轻移动设备的计算负载并提高任务处理效率。通过任务卸载技术优化深度神经网络成为边缘智能研究中的新方向。Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge这篇文章提出了协同推断的**,将深度神经网络进行分区,一部分层在移动端计算,而另一部分在云端计算。根据硬件平台、无线网络以及服务器负载等因素实现动态分区,降低时延以及能耗。本项目给出了边缘智能方面的相关论文,并且给出了一个Python语言实现的卷积神经网络协同推断实验平台。关键词:边缘智能(Edge Intelligence),计算卸载(Computing Offloading),CNN模型分区(CNN Partition),协同推断(Collaborative Inference),移动云计算(Mobile Cloud Computing)
revenol/DDLO
Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks
shiqiangw/service-migration-mdp
Code for paper "Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process"
AlbertoCastelo/resource-allocation-opt
Solving Resource Allocation problems with Mixed-Integer Linear Programming in Python
Coolzyh/Globecom2020-ResourceAllocationGNN
Code for Globecom2020 paper: Resource Allocation based on Graph Neural Networks in Vehicular Communications
quovadim/RL-Cache
thx/gogocode
GoGoCode is a transformer for JavaScript/Typescript/HTML based on AST but providing a more intuitive API.