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<div align="center"> <h1>The Hands-on Reinforcement Learning course πŸš€ </h1> <h2>From zero to HERO πŸ¦ΈπŸ»β€πŸ¦ΈπŸ½</h2> <h3><i>Out of intense complexities, intense simplicities emerge.</i></h3> <h4>-- Winston Churchill</h4> </div>

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Contents

Welcome to the course πŸ€—β€οΈ

Welcome to my step by step hands-on-course that will take you from basic reinforcement learning to cutting-edge deep RL.

We will start with a short intro of what RL is, what is it used for, and how does the landscape of current RL algorithms look like.

Then, in each following chapter we will solve a different problem, with increasing difficulty:

Ultimately, the most complex RL problems involve a mixture of reinforcement learning algorithms, optimizations and Deep Learning techniques.

You do not need to know deep learning (DL) to follow along this course.

I will give you enough context to get you familiar with DL philosophy and understand how it becomes a crucial ingredient in modern reinforcement learning.

Lectures

  1. Introduction to Reinforcement Learning
  2. Q-learning to drive a taxi πŸ†
  3. SARSA to beat gravity πŸ†
  4. Parametric Q learning to keep the balance πŸ’ƒ πŸ†
  5. Policy gradients to land on the Moon πŸ†

Wanna contribute?

There are 2 things you can do to contribute to this course:

  1. Spread the word and share it on Twitter, LinkedIn

  2. Open a pull request to fix a bug or improve the code readability.

Thanks ❀️

Special thanks to all the students who contributed with valuable feedback and pull requests ❀

Let's connect!

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