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boyu-ai/hands-on-rl

★ 4,728Jupyter NotebookAudience · researcherComplexity · 3/5Setup · moderate

TLDR

A Chinese-language tutorial series teaching reinforcement learning from scratch through Jupyter Notebooks, one notebook per chapter, each combining written explanation with runnable code covering mainstream algorithms.

Mindmap

mindmap
  root((hands-on-rl))
    What it covers
      RL fundamentals
      Mainstream algorithms
      Step by step chapters
    Format
      Jupyter Notebooks
      Text plus code
      Companion website
    Access options
      GitHub download
      Free video course
      Printed book
    Setup note
      Install older gym version
      For env errors
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Code map

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Things people build with this

USE CASE 1

Work through reinforcement learning algorithms chapter by chapter, running the code locally to see results immediately.

USE CASE 2

Use the companion website for a better reading experience than GitHub's notebook renderer provides.

USE CASE 3

Watch the free video course on Boyu Learning alongside the notebooks for a combined text and lecture format.

USE CASE 4

Reference specific algorithm implementations as working code examples when building your own RL experiments.

Tech stack

PythonJupyter Notebook

Getting it running

Difficulty · moderate Time to first run · 30min

The gym simulation library may require installing a specific older version to avoid runtime errors when launching environments.

License not stated in the explanation, check the repository directly before reusing.

In plain English

Hands-on Reinforcement Learning is a Chinese-language educational resource that teaches reinforcement learning from the ground up. Reinforcement learning is a branch of machine learning where a program learns to make decisions by trying actions and receiving rewards or penalties based on the results, similar to how a person learns through trial and experience. The repository contains a collection of Jupyter Notebooks, one per chapter, each combining written explanations with runnable code. The series starts from the basic definition of reinforcement learning and works through a range of mainstream algorithms used in the field today. The authors recommend visiting the project's companion website rather than reading the notebooks directly on GitHub, since GitHub's notebook rendering has limitations. The site version provides a better reading experience with the same content. The notebooks are also available for local download and execution. The README includes a note that if you encounter errors running the gym simulation environment, installing a specific older version of that library often fixes the problem. An accompanying video course is available on the Boyu Learning platform and is free for all learners. The content also exists as a printed book sold through major Chinese book retailers. Issues and improvement suggestions are accepted through the standard GitHub issue tracker.

Copy-paste prompts

Prompt 1
I am learning reinforcement learning using the boyu-ai/hands-on-rl notebooks. Show me step by step how to install the required Python packages and run the first chapter notebook locally.
Prompt 2
Using the hands-on-rl codebase as a reference, explain how Q-learning works and show me where in the notebook the Q-table update formula is implemented.
Prompt 3
Based on the boyu-ai/hands-on-rl series, write a minimal Python implementation of the REINFORCE policy gradient algorithm with comments explaining each step.
Prompt 4
I am getting an error running the gym environment in hands-on-rl. Show me how to install the specific older version of gym that fixes the compatibility issue mentioned in the README.
Prompt 5
Using the algorithm covered in hands-on-rl chapter on PPO, write a training loop for a custom environment with a discrete action space.
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