Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

Code implementation of "Cooperative Trajectory Design of Multiple UAV Base Stations with Heterogeneous Graph Neural Networks".

Multi-Agent Reinforcement Learning with JAX

GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach

Official Pytorch implementation of Soft-DRGN (IEEE trans on Mobile Computing 2022)

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A novel framework combining Graph Attention Networks (GAT) with Deep Reinforcement Learning (DRL) for optimizing energy consumption and performance in High-Performance Computing (HPC) job schedulin…

This repository features GASAC (Graph Attention Soft Actor–Critic), a decentralized framework for UAV swarms in disaster response, utilizing GAT-based perception, multi-critic SAC control, cluster …

This is the repository for the collection of Graph-based Deep Learning for Communication Networks.

《深入浅出图神经网络：GNN原理解析》配套代码

Code for Data Offloading in UAV-assisted Multi-access Edge Computing Systems: A Resource-based Pricing and User Risk-awareness Approach paper https://www.mdpi.com/1424-8220/20/8/2434/pdf

Trajectory Optimization and Computing Offloading Strategy in UAV-Assisted MEC System

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

Python implementation of DDQN multi-UAV data harvesting

Code for paper "Computation Offloading Optimization for UAV-assisted Mobile Edge Computing: A Deep Deterministic Policy Gradient Approach"

[JSAC 2018] Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

Code for the paper 'Multi-Agent Reinforcement Learning in NOMA-Aided UAV Networks for Cellular Offloading'

IEEE WCNC 2023: Deep Reinforcement Learning for Secrecy Energy-Efficient UAV Communication with Reconfigurable Intelligent Surfaces

Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing

Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning

Autonomous Navigation of UAV using Reinforcement Learning algorithms.

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…

This is a project about deep reinforcement learning autonomous obstacle avoidance algorithm for UAV.

RLGF is a general training framework suitable for UAV deep reinforcement learning tasks. And integrates multiple mainstream deep reinforcement learning algorithms(SAC, DQN, DDQN, PPO, Dueling DQN, …

Large-scale Satellite Networks Simulator (LSNS)

Master programming by recreating your favorite technologies from scratch.