Home Projects Practical_RL
Practical_RL
Jupyter Notebook

Practical_RL

A course in reinforcement learning in the wild

by yandexdataschool · GitHub
Stars
Forks
License
Created
Last commit
course-materialsdeep-learningdeep-reinforcement-learningUnlicenseJupyter Notebook
View on GitHub
In plain words

Learn about reinforcement learning through practical assignments and materials in an open course format.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
Practical_RL — GitHub preview card
📈 Star history
6.54k6.53k
2026-07-042026-08-31
📈 Track Practical_RL

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

A course in reinforcement learning in the wild

Practical_RL has 6.5k stars on GitHub. It has been forked 1.8k times. Practical_RL is written mainly in Jupyter Notebook. It has been in active development since 2017. Practical_RL is available under the Unlicense license. Its main topics are course-materials, deep-learning, deep-reinforcement-learning, git-course.

Frequently asked questions

What is Practical_RL?

A course in reinforcement learning in the wild

Is Practical_RL open source?

Practical_RL is an open-source project. It is released under the Unlicense license.

Is Practical_RL free?

Yes. Practical_RL is free and open source — you can use, modify and self-host it.

What license does Practical_RL use?

Practical_RL is available under the Unlicense license.

What language is Practical_RL written in?

Practical_RL is written mainly in Jupyter Notebook.

🏅 Maintainer of this project?
olud.ai badge — Practical_RL

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=yandexdataschool-practical-rl)](https://olud.ai/project/yandexdataschool-practical-rl.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.