Popular GitHub Repositories for Fitted Q Iteration

《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.

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

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)

A conversational Q&A agent configuration system, self-hosted deployment solutions, and a convenient all-in-one application SDK, allowing you to create intelligent Q&A bots for your GitHub repositories

✨ Add autocomplete and AI to your existing terminal on macOS & Linux

Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.

Read Pilot analyzes online articles and generate Q&A cards for you. Powered by OpenAI & Next.js.

Stock Trading Bot using Deep Q-Learning

A Solution Accelerator for the RAG pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. This includes most common requirements and best practices.

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

Smart, agentic, security AI terminal for developers and cloud practitioners