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

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

Heterogeneous Graph Neural Network

Reinforcement learning approach for job shop scheduling

PPO implementation of the DRL agent used in the paper "Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case"

The code and data will be published after accepting our paper

This repository contains the code of the deep MARL-based dynamic scheduling algorithms in job shop and flexible job shop

pytorch implementation of DQN/DDQN/Dueling_networ/D3QN for job shop scheudling problem

Master's Thesis - Graph Neural Networks for Compact Representation for Job Shop Scheduling Problems: A Comparative Benchmark

Official implementation of paper "Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling"

Deep reinforcement learning methods for flexible job shop scheduling problems

solve Flexible Job Shop Problem by Genetic Algorithm

A very simple deep reinforcement learning methods for flexible job shop scheduling problems

Deep Reinforcement Learning Based on Graph Neural Networks for Job-shop Scheduling

DRL for solving the dynamic job shop schedule problems with uncertain time

Evolutionary Trainer-based Deep Q-Network for Dynamic Flexible Job Shop Scheduling

A reimplementation of paper "Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning"