Popular GitHub Repositories for Chain Reaction Game

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

:sparkles::sparkles:Latest Advances on Multimodal Large Language Models

The official GitHub page for the survey paper "A Survey of Large Language Models".

? An exciting game of programming and Artificial Intelligence

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

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

A collection of LLM papers, blogs, and projects, with a focus on OpenAI o1 ? and reasoning techniques.

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

LLocalSearch is a completely locally running search aggregator using LLM Agents. The user can ask a question and the system will use a chain of LLMs to find the answer. The user can see the progress of the agents and the final answer. No OpenAI or Google API keys are needed.

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

Eko (Eko Keeps Operating) - Build Production-ready Agentic Workflow with Natural Language - eko.fellou.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.

The easiest tool for fine-tuning LLM models, synthetic data generation, and collaborating on datasets.

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

Reasoning in LLMs: Papers and Resources, including Chain-of-Thought, OpenAI o1, and DeepSeek-R1 ?

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

总结Prompt&LLM论文，开源数据&模型，AIGC应用

Simplifying reinforcement learning for complex game environments

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.