BURKOV

As you know, last year I resigned from my full-time job and become a professional technical book writer. I have plans for The Hundred-Page Books about reinforcement learning, computer vision, diffusion models, and more.

Not having a full-time job has put a significant strain on my savings, so if you love my books and are waiting for more, I would appreciate your continuous support.

I have finalized the offer for those who financially support me. Here's what I offer:

- As a Cool Supporter 10 🚀 ($10/month), you will get early access to new book chapters, exclusive updates on what to really care about in AI and why (only the important stuff, no hype), and a free e-book copy of all new books.

- As a Cool Supporter 30 🚀 ($30/month), you will get everything a Cool Supporter 10 🚀 gets, plus a signed hard copy of all new books.

The names of all Cool Supporters 🚀 will be mentioned in the Acknowledgment section of the new books as important contributors. Your name, printed in the book, will remain in history forever!

If you decide to become a Cool Supporter 🚀, you can choose from several platforms. Here's the list: linktr.ee/burkov

If you choose Substack, you will also get a subscription to a weekly AI newsletter without ads.

By the way, yes, if you purchase a copy, no matter what kind, you can reach out to me to get a coupon for $150 worth of free GPU credits on @LambdaAPI. (Thanks to the Lambda team for the continued support of my books!)

The book uses small-ish neural networks that would run on a CPU, but if you want to apply the algorithms from the book to environments like real world robot simulations or to finetune LLMs, you would need these GPUs.

Someone has gotten too excited 😄 The book on Leanpub is available with a suggested price of $50. You can use a slider and reduce the price to $25, which most readers do. This reader typed the price they wanted to pay by hand because the slider only allows you to go up to $100.

Thank you, my dear unknown friend. I hope you will enjoy the book at the $125 level or better!

TL;DR My new book is now available for purchase.

As you probably know, in February 2025, I resigned from my full-time job to become a full-time author. At the same time, I started working on a new book in The Hundred-Page Books series.

Then vibe coding happened, and I couldn't resist trying to build something this way. This resulted in ChapterPal, my AI-assisted reading and learning platform, which I've built without writing a single line of code. Today, with tools like Codex or Claude Code, it doesn't seem surprising, but when I started almost two years ago, I copied and pasted code back and forth to the chatbot window. It hallucinated solutions, rewrote what was working, refused to print the entire code, etc. It was a hell of an experience.

Last spring, ChapterPal's maintenance was automated enough to allow me to return to writing, which I gladly did.

And today, 20 months after I started, I finally finished my new book: The Hundred-Page Deep Reinforcement Learning Book.

Before sending the manuscript to print, I took a two-month break so that my brain could read it as if for the first time. For the entire last week, I was reading my book and I got chills so many times that I'm sure it's a very good book.

If you have been waiting for it, thanks for being patient. I hope you will see that the waiting was worth it.

Today, the book is available on Amazon as a paperback, hardcover, and Kindle edition, and on Leanpub as a PDF.

I'm currently preparing another hardcover edition, with different dimensions, for the rest of the online stores. For those who have my previous hundred-page books in their libraries, if you want the new hardcover book to be the same size as the previous two, you should probably wait for this non-Amazon edition to be published. I will announce this separately.

A couple of words about the book.

I liked the structure of my previous hundred-page book (the one on language models), where each chapter introduces a new algorithm that improves the previously introduced algorithms in some way. This makes the textbook read like a story rather than a collection of facts.

The Hundred-Page Deep Reinforcement Learning Book builds the subject step by step, beginning with reinforcement learning fundamentals and the Policy Gradient Theorem before developing REINFORCE, actor-critic methods, Generalized Advantage Estimation, and Proximal Policy Optimization (PPO). It then introduces off-policy learning and derives Deep Q-learning (DQN) from first principles. The final chapter shows how reinforcement learning applies to language models, including learning from human feedback and verifiable rewards with PPO and GRPO (remember DeepSeek R1).

The book comes with complete PyTorch implementations (on GitHub), the mathematical foundations needed to understand its content (in the appendices), and pointers for further study (in the last chapter). Additional learning resources are available on the book's wiki at thedrlbook.com/wiki.

The book was read and endorsed by several people I deeply admire. The back cover contains endorsements from Long Ouyang, a co-creator of InstructGPT and ChatGPT at OpenAI and Stephen Balaban, a co-founder and CTO at Lambda.

You can find all the endorsements at the book's website: thedrlbook.com.

I hope you enjoy this new release in the series as much as you enjoyed the other two. I can't wait for the readers' reviews to start coming.

(The Dark Edition is coming soon!)

I'm modernizing an old book on Android programming. The book is hands-on and has many screenshots. These screenshots are outdated and need to be recreated using a modern version of Android Studio.

I tasked Codex with reading the book and, following its explanations, reimplementing the Android project and retaking the screenshots.

It's been working for two days now. So far, so good: