[ICLR 2025] Graph Assisted Offline-Online Deep Reinforcement Learning (GOODRL) for Dynamic Workflow Scheduling (DWS)

[EAAI] A two-stage reinforcement learning-based approach for multi-entity task allocation.

Official Schlably Repository by the Institute for TMDT

Simulation Codes for Three Dynamic Pricing Schemes for Resource Allocation of Edge Computing for IoT Environment

Implementation of paper: Accelerating Traffic Engineering in Segment Routing Networks: A Data-Driven Approach (ICC 2022)

This repository contains code to preprocess data, classify anomalies, and visualize the results for detecting anomalies in telecommunications networks. The project aims to improve network reliabili…

基于贝叶斯网络的故障诊断算法

Packet routing simulation on a dynamic network using Shortest Path Routing, Q-learning, and Deep Q-learning

A Deep Reinforcement Learning Approach For Software-Defined Networking Routing Optimisation

A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization

Tobytywang的网站

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

CloudSimPy: Datacenter job scheduling simulation framework

Flax is a neural network library for JAX that is designed for flexibility.

Open Cloud Computing Data Mining Platform

Deep Reinforcement Learning Based Dynamic Resource Allocation in 5G Ultra-Dense Networks

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

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

An elegant PyTorch deep reinforcement learning library.

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

Deep reinforcement learning for mobile edge computing

Project: Deep Reinforcement Learning Based Resource Provisioning and Task Scheduling for Cloud Service Providers

Reproduce results of the research article "Deep Reinforcement Learning Based Resource Allocation for V2V Communications"

intial version

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 resour…

Massively Parallel Deep Reinforcement Learning. 🔥