Popular GitHub Repositories for Meta Rl

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.

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

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

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

An elegant PyTorch deep reinforcement learning library.

Automated Machine Learning with scikit-learn

Massively Parallel Deep Reinforcement Learning. ?

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

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

Streamer-Sales 销冠 —— 卖货主播 LLM 大模型??，一个能够根据给定的商品特点从激发用户购买意愿角度出发进行商品解说的卖货主播大模型。??内含详细的数据生成流程? ?另外还集成了 LMDeploy 加速推理?、RAG检索增强生成 ?、TTS文字转语音?、数字人生成 ?、 Agent 使用网络查询实时信息?、ASR 语音转文字??、Vue 生态搭建前端?、FastAPI 搭建后端??、Docker-compose 打包部署?

Modularized Implementation of Deep RL Algorithms in PyTorch

Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

A PyTorch Library for Meta-learning Research

MuZero

Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms