Popular GitHub Repositories for Game Documentation

Code and documentation to train Stanford's Alpaca models, and generate the data.

A game theoretic approach to explain the output of any machine learning model.

Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls)

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

Best Practices, code samples, and documentation for Computer Vision.

? An exciting game of programming and Artificial Intelligence

Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!

Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).

? Refly is an open-source AI-native creation engine. Its intuitive free-form canvas interface combines multi-threaded dialogues, artifact, AI knowledge base integration, chrome extension clip & save, contextual memory, intelligent search, WYSIWYG AI editor and more, empowering you to effortlessly transform ideas into production-ready content.

Single powerful tool to convert ANY documentation website into a Claude skill

H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/

Simplifying reinforcement learning for complex game environments

[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | 斗地主AI

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

C++ Action Roguelike in Unreal Engine 5.

TensorFlow 最新官方文档中文版

An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

Easiest and laziest way for building multi-agent LLMs applications.

Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.

MiniMax-01 is a simple implementation of the MiniMax algorithm, a widely used strategy for decision-making in two-player turn-based games like Tic-Tac-Toe. The algorithm aims to minimize the maximum possible loss for the player, making it a popular choice for developing AI opponents in various game scenarios.