Edenguopy

Edenguopy's Stars

• jiwentao2004/IntellignetAGVSchedulingSystem

  适用于多种磁导航AGV的调度系统。开放式接口，允许接入MES、ERP、WMS、WCS等系统。

  2411

• ShangGuanPF/WMS

  融合物联网标识与定位技术的仓储管理系统（基于Qt平台、MySQL 数据库以及UWB定位模块开发）

  Language:C++3624

• null-xyj/CoBFormer

  Implementation of ICML'24 Paper "Less is More: on the Over-Globalizing Problem in Graph Transformers"

  Language:Python393

• alexberndt/sadg-controller

  Implementation of the SADG RHC feedback control scheme to reduce route completion times of delayed agents following MAPF plans.

  Language:Python216

• WhereIsHeroFrom/cpp_base

  C++零基础入门

  Language:C++6312

• AIRI-Institute/learn-to-follow

  Language:C++12

• andy123t/GMCMthesis

  2024 年研究生数学建模 LaTeX 模板

  Language:TeX5016

• clashdownload/Clash

  Clash官网各版本Clash下载地址及备份下载地址

  81275

• Lodz97/Multi-Agent_Pickup_and_Delivery

  Implementations of various algorithms used to solve the problem of Multi-Agent Pickup and Delivery (a generalization of Multi-Agent Path Finding).

  Language:Python5515

• bpriviere/glas

  Language:C++4711

• lcpmgh/colors

  学术期刊配色推荐器

  Language:R17214

• microsoft/OptiGuide

  Large Language Models for Supply Chain Optimization

  Language:Jupyter Notebook35855

• wu6u3/async_ppo

  Asynchronous Proximal Policy Optimization with TensorFlow and OpenAI Gym

  Language:Jupyter Notebook81

• Tviskaron/pogema-baselines

  PPO and PyMARL baseline for Pogema environment

  Language:Python205

• alex-petrenko/sample-factory

  High throughput synchronous and asynchronous reinforcement learning

  Language:Python853114

• Cognitive-AI-Systems/when-to-switch

  "When to Switch" Implementation: Addressing the PO-MAPF challenge with RePlan & EPOM policies. This repo includes search-based re-planning, reinforcement learning techniques, and three mixed policies for pathfinding in partially observable multi-agent environments. 🤖🛤️

  Language:Python282

• Cognitive-AI-Systems/mats-lp

  [AAAI-2024] MATS-LP addresses the challenging problem of decentralized lifelong multi-agent pathfinding. The proposed approach utilizes a combination of Monte Carlo Tree Search and reinforcement learning for resolving conflicts.

  Language:C++23

• krahets/LeetCode-Book

  《剑指 Offer》 Python, Java, C++ 解题代码，LeetBook《图解算法数据结构》配套代码仓

  Language:Java6.8k795

• m1ntzz/ppo_path_planning

  基于ppo的路径规划

  Language:Python284

• itcharge/LeetCode-Py

  ⛽️「算法通关手册」：超详细的「算法与数据结构」基础讲解教程，从零基础开始学习算法知识，850+ 道「LeetCode 题目」详细解析，200 道「大厂面试热门题目」。

  Language:Python6.3k1.1k

• merschformann/RAWSim-O

  A simulation framework for Robotic Mobile Fulfillment Systems

  Language:C#20267

• mail-ecnu/PICO

  An algorithm for exploiting Reinforcement Learning (RL) on Multi-agent Path Finding tasks.

  Language:Python5614

• HUOJIAXI/Dual-layer-Multi-robot-Path-Planning-in-Narrow-lane-Warehousing-Environments

  [Simulation data and videos]Dual-layer Multi-robot Path Planning in Narrow-lane Warehousing Environments Using Integer Programming and Fast Feasibility Heuristics

  2

• Cognitive-AI-Systems/pogema

  POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.

  Language:Python21024

• Cognitive-AI-Systems/learn-to-follow

  [AAAI-2024] Follower: This study addresses the challenging problem of decentralized lifelong multi-agent pathfinding. The proposed Follower approach utilizes a combination of a planning algorithm for constructing a long-term plan and reinforcement learning for resolving local conflicts.

  Language:C++372

• PathPlanning/AA-SIPP-m

  Algorithm for prioritized multi-agent path finding (MAPF) in grid-worlds. Moves into arbitrary directions are allowed (each agent is allowed to follow any-angle path on the grid). Timeline is continuous, i.e. action durations are not explicitly discretized into timesteps. Different agents' size and moving speed are supported. Planning is carried out in (x, y, \theta) configuration space, i.e. agents' orientation are taken into account.

  Language:C++11040

• marmotlab/SCRIMP

  offical code of paper 'SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfinding'

  Language:Python4115

• Edenguopy/Warehouse_MAPF_Benchmark

  This project provides a training and testing environment for multi robot path planning in warehousing environments

  2

• Kei18/pibt2

  Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding (AIJ-22)

  Language:C++6430

• mathworks/MATLAB-Simulink-Challenge-Project-Hub

  This MATLAB and Simulink Challenge Project Hub contains a list of research and design project ideas. These projects will help you gain practical experience and insight into technology trends and industry directions.

  Language:MATLAB1.4k289