Туториал по Reinforcement Learning без математики: только код для программистов

A tutorial on machine learning with reinforcement (Reinforcement Learning) was published on Habr on July 6, 2026 — an author from Cinimex undertook to fill a long-standing gap: to explain RL to programmers through pure code, completely excluding mathematical derivations.

Why Most ML Tutorials Are Difficult to Read

Most educational materials on ML are written by specialists with mathematical training — and this is natural. Data Scientists and ML engineers typically enter the profession through mathematics and statistics, not through programming. Their code reflects this mindset: structured like a mathematical proof, densely interwoven with terms and abstractions understandable only to those who already know the theory.

For an experienced programmer without deep mathematical training, such code is unreadable — and it's not a matter of syntax. Individual lines are clear, but why they execute in this specific order with these parameters remains unclear. The complexity of language constructs overlaps with a non-trivial theoretical foundation: as a result, even a conscientious tutorial becomes a set of formulas whose meaning cannot be recovered without prior knowledge.

"If you're not familiar with the theory, then it's sometimes simply

impossible to guess from the code why the executed actions are needed," the author explains.

The paradox is that programmers are precisely the largest audience interested in applying ML to real products. Meanwhile, educational content written from a developer's perspective rather than a researcher's remains extremely scarce.

What Changed Since 2019 — and What Hasn't

The author became interested in ML and AI in 2019 — a period when the topic ceased to be the domain of academic science and entered the technology mainstream. Since then, the amount of publicly available articles and code examples has grown many times over: courses appeared in Russian, thematic channels and communities with thousands of participants emerged.

But one thing remained unchanged: the coding style of examples and their mathematical nature. There is more content — but the entry threshold for a programmer without serious mathematical training remained the same.

The tutorial proposes a different approach:

• no mathematical formulas — only code
• each action is explained from the perspective of its meaning, not from the derivation of a theorem
• the writing style is close to real projects, not academic papers
• the material is aimed at those with school and university-level mathematics

This approach — explanation through production code — became a standard in web development long ago, but in ML it is still an exception rather than a rule.

Why Reinforcement Learning Specifically

Reinforcement Learning is one of the most complex areas of ML. An agent learns not from labeled data, but through interaction with an environment: it tries actions, receives a reward or penalty signal, and learns to maximize long-term reward. It is RL that underlies RLHF (Reinforcement Learning from Human Feedback) — the key method by which modern large language models, including GPT and Claude, are aligned.

This makes understanding the basic principles of RL useful not only for researchers. Developers working with AI systems and integrating language models into products gain a deeper understanding of what they work with daily.

At the same time, quality explanations of RL in a "for programmers" style are still almost non-existent — neither in Russian nor in English. Most introductory materials are either too academic or too superficial. The Habr tutorial offers something in between: depth without a mathematical barrier.

What This Means

Educational content on ML is gradually shifting towards an engineering audience. Authors who can explain mathematically complex concepts through code without loss of meaning are few and far between. If the tutorial maintains the stated style throughout the article, it could become one of the most useful resources for Russian-speaking developers taking their first steps in ML.