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Evolutionary Trainer-based Deep Q-Network for Dynamic Flexible Job Shop Scheduling
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PmHub，一个基于 SpringCloud & LLM 的智能项目管理系统，该项目旨在帮助小伙伴们快速掌握微服务/分布式项目的架构设计和开发流程。如果你想在校招或者社招中拿到一个满意的 offer，PmHub 将是一个非常 nice 的选择😄。
使用 Typst 编写的中文简历, 语法简洁, 样式美观, 开箱即用, 可选是否显示照片
A very simple deep reinforcement learning methods for flexible job shop scheduling problems
The code and data will be published after accepting our paper
PPO implementation of the DRL agent used in the paper "Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case"
Deep reinforcement learning methods for flexible job shop scheduling problems
A reimplementation of paper "Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning"
Official implementation of paper "Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling"
Master's Thesis - Graph Neural Networks for Compact Representation for Job Shop Scheduling Problems: A Comparative Benchmark
A benchmarking repo with various solution methods to various machine scheduling problems
pytorch implementation of DQN/DDQN/Dueling_networ/D3QN for job shop scheudling problem
Address the JSP problem through DRL, including mlp, gcn, transformer policies.
Deep Reinforcement Learning Based on Graph Neural Networks for Job-shop Scheduling
Reinforcement learning approach for job shop scheduling
Representation-Learning-on-Heterogeneous-Graph
solve Flexible Job Shop Problem by Genetic Algorithm
Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.
This repository contains the code of the deep MARL-based dynamic scheduling algorithms in job shop and flexible job shop
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
DRL for solving the dynamic job shop schedule problems with uncertain time