Popular GitHub Repositories for Shapley Q Value

For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

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

《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调（全参数/Lora）、部署国内外开源大模型（LLM）/多模态大模型（MLLM）教程

强化学习中文教程（蘑菇书?），在线阅读地址：https://datawhalechina.github.io/easy-rl/

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

BELLE: Be Everyone's Large Language model Engine（开源中文对话大模型）

KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.

A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.

Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through our open source R&D automation tool RD-Agent, which lets AI drive data-driven AI.

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

The unofficial python package that returns response of Google Bard through cookie value.

Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)

The AI Datastore for Schemas, BLOBs, and Predictions. Use with your apps or integrate built-in Human Supervision, Data Workflow, and UI Catalog to get the most value out of your AI Data.

Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)

Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback

Review-Gate V2 is a powerful rule for the Cursor IDE that helps you get up to 5x more value from your monthly requests. It creates an interactive loop where the AI waits for your follow-up commands—via text, voice, or image upload—allowing you to perform deep, iterative work all within a single request.