hydelovegood

hydelovegood's Stars

• iCGY96/awesome_OpenSetRecognition_list

  A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition

  1.1k146

• Shanghai-Digital-Brain-Laboratory/DB-Football

  A Simple, Distributed and Asynchronous Multi-Agent Reinforcement Learning Framework for Google Research Football AI.

  Language:Python9513

• leslievan/semi-utils

  一个批量添加相机机型和拍摄参数的工具，后续「可能」添加其他功能。

  Language:Python1.2k126

• PKU-MARL/HARL

  Official implementation of HARL algorithms based on PyTorch.

  Language:Python49962

• Intelligent-Driving-Laboratory/GOPS

  General Optimal control Problem Solver (GOPS), an easy-to-use PyTorch reinforcement learning solver package for industrial control.

  Language:Python19837

• 3b1b/manim

  Animation engine for explanatory math videos

  Language:Python69.6k6.1k

• Lewiscowles1986/py-call-graph

  pycallgraph is a Python module that creates call graphs for Python programs.

  Language:Python756

• aladdinpersson/Machine-Learning-Collection

  A resource for learning about Machine learning & Deep Learning

  Language:Python7.6k2.7k

• OpenRL-Lab/TiZero

  Code accompanying the paper "TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play" (AAMAS 2023) 足球游戏智能体

  Language:Python497

• Unity-Technologies/ml-agents

  The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

  Language:C#17.1k4.2k

• thomyphan/messy_smac

  MessySMAC - A Modified StarCraft MultiAgent Challenge with Configurable State Uncertainty

  Language:Python9

• Kaixhin/Rainbow

  Rainbow: Combining Improvements in Deep Reinforcement Learning

  Language:Python1.6k283

• Curt-Park/rainbow-is-all-you-need

  Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow

  Language:Jupyter Notebook1.9k333

• KwaiVGI/LivePortrait

  Bring portraits to life!

  Language:Python12.7k1.3k

• WengLean/hands-on-research-tutorial

  《动手做科研》面向科研初学者，一步一步地展示如何入门人工智能科研

  Language:Jupyter Notebook2387

• PKU-TANGENT/nlp-tutorial

  NLP新手入门教程

  Language:Python1.1k115

• chen08209/FlClash

  A multi-platform proxy client based on ClashMeta,simple and easy to use, open-source and ad-free.

  Language:Dart10.1k596

• PKUFlyingPig/cs-self-learning

  计算机自学指南

  Language:HTML57.3k6.8k

• codecrafters-io/build-your-own-x

  Master programming by recreating your favorite technologies from scratch.

  Language:Markdown306k28.7k

• rasbt/LLMs-from-scratch

  Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step

  Language:Jupyter Notebook30.3k3.6k

• MatsuriDayo/NekoBoxForAndroid

  NekoBox for Android / sing-box / universal proxy toolchain for Android

  Language:Kotlin11.6k977

• aoaostar/legado

  阅读APP书源

  Language:HTML2.8k88

• corl-team/CORL

  High-quality single-file implementations of SOTA Offline and Offline-to-Online RL algorithms: AWAC, BC, CQL, DT, EDAC, IQL, SAC-N, TD3+BC, LB-SAC, SPOT, Cal-QL, ReBRAC

  Language:Python47820

• vwxyzjn/cleanrl

  High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

  Language:Python5.6k635

• darcula1993/diffusion-models-class-CN

  Materials for the Hugging Face Diffusion Models Course

  Language:Jupyter Notebook16621

• liuqh16/LAG

  An environment based on JSBSIM aimed at one-to-one close air combat.

  Language:Python28292

• CosmosShadow/gptpdf

  Using GPT to parse PDF

  Language:Python3k228

• ComposioHQ/composio

  Composio equip's your AI agents & LLMs with 100+ high-quality integrations via function calling

  Language:Python11.1k4.3k

• tencent-ailab/mini-hok

  Mini HoK: a novel MARL benchmark based on the popular mobile game, Honor of Kings, to address limitations in existing environments such as complexity and accessibility.

  Language:Python31

• Amanda2024/GCS_aamas337

  The code for AAMAS2022 《GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning》

  Language:Python388