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课程视频、PPT和源代码:侯捷C++系列;台大郭彦甫MATLAB
Deep Reinforcement Learning
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)
computation offloading in mobile edge computation using Reinforcement Learning
joint computation offloading and resource allocation in Internet of Vehicle
Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
Pytorch implementation of the MARL algorithm, MADDPG, which correspondings to the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments".
基于深度强化学习的MEC计算卸载与资源分配
使用Drl来解决多智能体卸载问题
I build this Mobile Edge Computation simulating environment all by myself, and use the costomized ddpg reinforcement learning algorithm to make offloading decision.
Achieve a tiny STL in C++11
基于深度强化学习的部分计算任务卸载延迟优化
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
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
Clean and robust implementations of Reinforcement Learning algorithms by Pytorch
scalable multi agents reinforcement learning
:tangerine: tensorflow、keras 学习;python 基础;numpy、matplotlib、scikit-learn等;python图像处理(含opencv、gdal等);matlab 学习。
:fire: Linux下C++轻量级Web服务器
Webbench是Radim Kolar在1997年写的一个在linux下使用的非常简单的网站压测工具。它使用fork()模拟多个客户端同时访问我们设定的URL,测试网站在压力下工作的性能,最多可以模拟3万个并发连接去测试网站的负载能力。官网地址:http://home.tiscali.cz/~cz210552/webbench.html
Uubuntu 20 C++版本的web服务器
C++ Linux WebServer服务器
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Open source projects and samples from Microsoft.
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Data-Driven Documents codes.
China tencent open source team.