dongtianshuo

dongtianshuo's Stars

• google-research/reincarnating_rl

  [NeurIPS 2022] Open source code for reusing prior computational work in RL.

  Language:Python9111

• bukatea/reuse-edge

  Example applications implementing compute reuse for edge networks

  Language:C++23

• Zahra-FallahMMA/DRL_Offload_Allocation

  Deep Reinforcment Learning based offloading and allocation

  Language:Jupyter Notebook1

• czgdp1807/MECOptimalOffloading

  Optimization of Offloading Scheme Algorithm for Large Number of Tasks in Mobile-Edge Computing

  Language:Python16537

• mobinets/task-offloading-edge-computing

  Simulation code for multi-user offloading in edge computing newtorks

  Language:MATLAB25557

• KashifIbrahimNagi/FI-RBTOM-for-EC

  Fuzzy Inference Rule Based Task Offloading Model (FI-RBTOM) for Edge Computing

  1

• ZWLab23/Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV-Networks

  Language:Python203

• francmeister/Masters-Research-Project

  Machine Learning-based Computation Offloading in Energy-Harvesting IoT Networks

  Language:Python4

• TesfayZ/CCM_MADRL_MEC

  The source code for the paper titled Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing

  Language:Python536

• UIC-JQ/IOPO

  first paper project owned by Jianqiu Wu, Paper is in preparation

  Language:Python81

• Lizhi-sjtu/DRL-code-pytorch

  Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

  Language:Python1k171

• RCL98/HiPPO

  HiPPO: Human Instruction Proximal Policy Optimization

  Language:Jupyter Notebook1

• sweetice/Deep-reinforcement-learning-with-pytorch

  PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

  Language:Python3.9k841

• marload/DeepRL-TensorFlow2

  🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2

  Language:Python598141

• novicasarenac/car-racing-rl

  Reinforcement learning algorithms A2C, A3C and DQN

  Language:Python144

• lyumeilin01/Comparing-RL-models-on-Atari-games

  Comparing A2C and PPO performances on various Atari games.

  Language:Python1

• a13xe/PolicyGradientAlgorithms

  Comparing VPG, TRPO and PPO from Policy Gradient family

  Language:Python2

• sureshkumarvrs8/Efficient-Task-Offloading-in-VEC-using-DQN

  Language:Python61

• akhajooyee/pilotNet

  Task offloading in a vehicular network with edge servers

  Language:Python1

• xuaikun/TransEdge

  TransEdge: Task Offloading with GNN and DRL in Edge Computing-Enabled Transportation System

  331

• ihsanskku/vto

  Task offloading in vehicular edge computing networks

  Language:Python171

• neardws/Game-Theoretic-Deep-Reinforcement-Learning

  Code of Paper "Joint Task Offloading and Resource Optimization in NOMA-based Vehicular Edge Computing: A Game-Theoretic DRL Approach", JSA 2022.

  Language:Python21947

• EscapeTheWind/Edge-Computing-and-Caching-Optimization-based-on-PPO-for-Task-Offloading-in-RSU-assisted-IoV

  Language:Python499

• ZWLab23/Delay-and-Battery-Degradation-Optimization-based-on-PPO-for-Task-Offloading-in-RSU-assisted-IoV

  Language:Python10

• jungyeonkoh/IoV-Computation-Offloading

  Language:Python216

• TD3-ALGORITHM/TD3-APPROACH

  A TD3 APPROACH IN OFFLOADING TRAFFIC FOR UAV-ASSISTED MOBILE EDGE COMPUTING

  Language:Python201

• 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

  Language:Python14328

• 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)

  Language:Python279107

• Cyclotron2333/Task-Offloading-and-Resource-Allocation-for-Multi-Server-Mobile-Edge-Computing-Networks

  Language:MATLAB24644

• ImanRHT/QECO

  A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing

  Language:Python7213