This repository is a curated collection of Chinese-language resources for Reinforcement Learning (RL), targeting students and researchers seeking to learn or deepen their understanding of the field. It provides links to foundational books, university courses, and practical code implementations, aiming to offer a comprehensive learning path from introductory concepts to advanced deep RL topics.

How It Works

The collection is organized by resource type, including seminal books like Sutton's "Reinforcement Learning: An Introduction" and lecture materials from prominent universities such as UCL, Stanford, UC Berkeley, and CMU. It also highlights the OpenAI Spinning Up documentation and code, and tutorials on Multi-Agent Reinforcement Learning. The resources are presented with direct links to course pages, slides, and relevant code repositories, facilitating easy access for self-study.

Quick Start & Requirements

• Access to online resources (links provided).
• No specific installation required; it's a curated list of external materials.
• Familiarity with Python and deep learning frameworks (TensorFlow, PyTorch) is beneficial for code examples.

Highlighted Details

• Comprehensive coverage of foundational RL concepts and advanced Deep RL topics.
• Links to lecture slides and videos from leading academic institutions.
• Inclusion of practical code examples and tutorials, such as OpenAI Spinning Up.
• Specific focus on Multi-Agent Reinforcement Learning with contributions from notable researchers.

Maintenance & Community

The repository was last updated in November 2018. It lists contributions from academic institutions and researchers, including UCL, Stanford, UC Berkeley, CMU, and Taiwanese research groups. There are no explicit links to community forums or active development channels mentioned.

Licensing & Compatibility

The repository itself does not host code or original content, but rather links to external resources. The licensing of the linked materials would depend on their original sources. Compatibility for commercial use or closed-source linking would require checking the licenses of the individual linked resources.

Limitations & Caveats

The resources are primarily from 2018 and earlier, meaning they may not cover the latest advancements in RL research. The collection is a curated list and does not provide an integrated learning platform or interactive environment.