yyds-xtt

PeerJ-Computer-Science

DRL-MEC

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

DRL-for-edge-computing

AFLDDPG

* Wu Q, Wang S, Fan P, et al. Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing[J]. arXiv preprint arXiv:2304.02832, 2023. 链接： https://arxiv.org/abs/2304.02832 代码： https://github.com/qiongwu86/AFLDDPG

drone-network-onos-and-mininet

[12] Prioritization Based Task Offloading in UAV-Assisted Edge Networks 作者：Kalinagac O, Gür G, Alagöz F. 出处：Sensors, 2023 摘要：在流量激增、覆盖问题和低延迟要求等苛刻的操作条件下，地面网络可能无法为用户和应用程序提供预期的服务水平。此外，当自然灾害或自然灾害发生时，现有网络基础设施可能崩溃，给服务区域的应急通信带来巨大挑战。为了在瞬态高服务负载情况下提供无线连接并促进容量提升，需要替代或辅助快速部署网络。由于其高机动性和灵活性，无人驾驶飞行器(UAV)网络非常适合此类需求。在这项工作中，我们考虑由配备无线接入点的无人机组成的边缘网络。这些软件定义的网络节点在边缘到云

Edge-Caching-Based-on-Multi-Agent-Deep-Reinforcement-Learning-and-Federated-Learning

JODRL-PP

Repository for 'Privacy-Preserving Offloading Scheme in Multi-Access Edge Computing Based on MADRL'

MIMO-D2D

[4] A Power Allocation Scheme for MIMO-NOMA and D2D Vehicular Edge Computing Based on Decentralized DRL 作者：Long D, Wu Q, Fan Q, et al. 出处：Sensors 摘要：在车辆边缘计算(VEC)中，一些任务可以在本地或在基站(BS)或附近车辆的移动边缘计算(MEC)服务器上处理。事实上，任务是否卸载取决于车辆对基础设施(V2I)和车辆对车辆(V2V)通信的状态。在本文中，考虑了基于设备到设备(D2D)的V2V通信和基于多输入多输出和非正交多址(MIMO-NOMA)的V2I通信。在实际通信场景中，基于MIMO-NOMA的V2I通信信道条件不确定，任务到达随机，导致VE

UCMEC-mmWave-Fronthaul

Simulation code of our paper ''Towards Decentralized Task Offloading and Resource Allocation in User-Centric Mobile Edge Computing''

DEAT

Achieving Fast Environment Adaptation of DRL-Based Computation Offloading in Mobile Edge Computing

QoE_Offloading_DRL

QoE-Driven Task Offloading in Mobile Edge Computing with Deep Reinforcement Learning

Chengdu_BSs

The location of the base station in the city of Chengdu

Chengdu_Taxi_Track

Chengdu Taxi Track

DVRPSR_PPO

DRL for Dynamic Vehicle Routing Problem with stochastic customer requests

HADRL

Deep Reinforcement Learning for UAV Routing in The Presence of Multiple Charging Stations

MaDRLAM

Multi-Agent Deep Reinforcement Learning for Task Offloading in GDMSs

ML-RL-simulations

A multi-layer guided reinforcement learning-based tasks offloading in edge computing - Simulations

MTFNN-CO

Official TensorFlow implementation for the paper "Computation Offloading in Multi-Access Edge Computing: A Multi-Task Learning Approach" and "A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading"

Rome_MVs

The mobile vehicles (MVs) trajectories in the Rome city.

Deep-Reinforcment-Learning

This is the repository for the deep reinforcement learning in classic and novel wireless communication scnarios.

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

edge_simulation1

Designing a Deep Q-Learning Model with Edge-Level Training for Multi-Level Task Offloading in Edge Computing Networks

STMRL

A spatial-temporal multi-agent reinforcement learning framework (STMRL) to perform distributed decision-making in multi-edge empowered computation offloading systems

UCB_MARL

The simulation codes of a provably efficient multi-agent reinforcement learning algorithm with a near-optimal regret bound in industrail data collection.

cc-

MDVO

Mean-field reinforcement learning for decentralized task offloading in vehicular edge computing

mec_morl_multipolicy

The python code for paper "Multi-objective Deep Reinforcement Learning for Mobile Edge Computing"

morl-baselines

Multi-Objective Reinforcement Learning algorithms implementations.

Safe-Policy-Optimization

This is a benchmark repository for safe reinforcement learning algorithms

UCMEC_env

Deep Reinforcement Learning Environments for User-Centric Mobile Edge Computing