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The Little Book of Reinforcement Learning

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Why This Matters

The Little Book of Reinforcement Learning provides an accessible introduction to reinforcement learning algorithms, supported by practical implementations in PyTorch. This resource is valuable for both learners and practitioners aiming to deepen their understanding and develop applications in AI and machine learning. Its open-source nature encourages community collaboration and continuous improvement, fostering innovation in the tech industry.

Key Takeaways

The Little Book of Reinforcement Learning

This is the associated GitHub page of the Little Book of Reinforcement Learning.

This book is a short introduction to Reinforcement Learning, from the basics to applied algorithms.

In this repo, along with the book itself, you can find the supplementary material of the book. More precisely:

under the algos/ folder, the Pytorch-based implementation of the different algorihms covered in the book, from MC to PPO.

folder, the Pytorch-based implementation of the different algorihms covered in the book, from MC to PPO. under the supplementary/ folder, you can find detailed explanations and rigorous proofs for the dynamic programming algorithms briefly covered in the book. This is a document I wrote in 2021.

More material is subject to be added along the way in this repo.

You can print one for youself here.

Versions of the book :

V1 (June 2026)

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