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😘 让你“爱”上 GitHub，解决访问时图裂、加载慢的问题。（无需安装）
The agent that grows with you
This repository is the official implementation of the paper “Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning”. IEEE Transactions on Neural Networks and Learning…
A Deep Reinforcement Learning Algorithm with Multi-view Graph Attention Mechanism for Flexible Job Shop Scheduling Problem
A Python implementation of NSGA-II for multi-objective FJSP, optimizing makespan and energy consumption.
Deep Reinforcement Learning-assisted Memetic Algorithm for Distributed Flexible Job Shop Scheduling
In this project multi agent reinforcement learning is trained for flexible job scheduling problem.
Solve production scheduling problems with constraint programming in Python.
Result files and instances for Flexible Job-Shop Scheduling Problem with Transportation resources
Repo containing the code for our paper Neural Multi-Objective Combinatorial Optimization for Flexible Job Shop Scheduling Problems, accepted at ICLR 2026.
It is the FJSP-LB-MK project. This project includes synthetic and real production datasets, a unified simulation environment, a relational graph-based DRL method, and multiple baselines (OR-Tools, …
一个基于nano banana pro🍌的原生AI PPT生成应用，迈向＂Vibe PPT＂; 支持上传任意模板图片，上传任意素材&智能解析，一句话/大纲/页面描述自动生成PPT，口头修改指定区域、一键导出可编辑ppt - An AI-native slides generator based on nano banana pro🍌
In this repo, GA, SA, and PSO algorithms are implemented to solve Job-shop Scheduling Problem(JSP) and Flexible Job-shop Scheduling Problem (FJSP) problem.
Lumina Robotics Talent Call | Lumina社区具身智能招贤榜 | A list for Embodied AI / Robotics Jobs (PhD, RA, intern, etc
[Lumina具身智能社区] 具身智能技术指南 Embodied-AI-Guide
[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | 斗地主AI
Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
The public benchmark instances of flexible job shop scheduling problem
we want to create a repo to illustrate usage of transformers in chinese
快速上手AI理论及应用实战：基础知识、Transformer、NLP、ML、DL、竞赛。含大量注释及数据集，力求每一位能看懂并复现。
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