OopsCCK

OopsCCK's Stars

• kangjianwei/Data-Structure

  《数据结构》-严蔚敏.吴伟民-教材源码与习题解析

  Language:C3.5k983

• neurips2020submission11699/metarl

  Neurips 2020 Submission 11699

  Language:Python4

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

  Language:Python488

• diaoenmao/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients

  [ICLR 2021] HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

  Language:Python14133

• yaserkl/RLSeq2Seq

  Deep Reinforcement Learning For Sequence to Sequence Models

  Language:Python767160

• chengshengchan/model_compression

  Implementation of model compression with knowledge distilling method.

  Language:Python344100

• thu-ml/tianshou

  An elegant PyTorch deep reinforcement learning library.

  Language:Python7.7k1.1k

• jungyeonkoh/IoV-Computation-Offloading

  Language:Python216

• nju-cn/MEC_offloading_ADQN

  采用强化学习来实现计算卸载

  Language:Python10012

• yyds-xtt/DRL-MEC

  Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing

  Language:Python91

• BuptMecMigration/Edge-Computing-Dataset

  MEC，Edge service, Edge Application, Service Computing.

  5713

• hetianzhang/Edge-DataSet

  Dataset for IoT, Fog and Edge-based experiments

  4711

• qiyu3816/MTFNN-CO

  [IEEE TMC 2020] "Computation Offloading in Multi-Access Edge Computing: A Multi-Task Learning Approach" and [IEEE GlobeCom 2023] "A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading" by TensorFlow

  Language:Python5110

• jyzgh/FedLPS

  Language:Python71

• 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:Python476

• Coolzyh/Globecom2020-ResourceAllocationGNN

  Code for Globecom2020 paper: Resource Allocation based on Graph Neural Networks in Vehicular Communications

  Language:Python4511

• 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

  Language:Python312100

• JedMills/MTFL-For-Personalised-DNNs

  Code for 'Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing', published in IEEE TPDS.

  Language:Python8120

• x810366146/Computition-Offloading-and-Resource-Allocation-with-PPO

  This project utilizes deep reinforcement learning methods for computation offloading and resource allocation in edge computing systems.

  Language:Python204

• OCEAN-HL/DRL-based-long-term-resource-management-in-MEC-network

  Language:Python221

• paulosevero/argos

  Privacy-aware service migration strategy for edge computing environments

  Language:Python132

• yyds-xtt/DRL-Based-Long-Term-Resource-Planning

  Paper publised in TNSM entitled "DRL-Based Long-Term Resource Planning for Task Offloading Policies in Multi-Server Edge Computing Networks"

  6110

• lehongwen/edge-computing

  Awesome-EdgeComputing：边缘计算技术指南和资源列表

  7635

• RobvanGastel/meta-rl-algorithms

  A collection of Meta-Reinforcement Learning algorithms in PyTorch

  Language:Python314

• tristandeleu/pytorch-maml-rl

  Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch

  Language:Python815157

• twni2016/pomdp-baselines

  Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022

  Language:Python28941

• Grottoh/Deep-Active-Inference-for-Partially-Observable-MDPs

  Language:Python212

• xiaolincoder/CS-Base

  图解计算机网络、操作系统、计算机组成、数据库，共 1000 张图 + 50 万字，破除晦涩难懂的计算机基础知识，让天下没有难懂的八股文！🚀 在线阅读：https://xiaolincoding.com

  13.8k1.8k

• komeilmoghaddasi/IoT_Task_Offloading_Dataset

  This synthetic dataset represents a scenario of 10,000 interactions between different types of IoT devices and edge servers. if you want to use this dataset please cite article: https://doi.org/10.1007/s10586-023-04195-4

  9

• Livioni/DAG_Generator

  Random Directed Acyclic Graph Generator

  Language:Python457