Contents

• Welcome to the course
• Lectures
• Wanna contribute?
• Let's connect!

Welcome to the course 🤗 ❤️

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:

• 🏆 easy
• 🏆 🏆 medium
• 🏆 🏆 🏆 hard

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

0. Introduction to Reinforcement Learning
1. Q-learning to drive a taxi 🏆
2. SARSA to beat gravity 🏆
3. Parametric Q learning to keep the balance 💃 🏆
4. Policy gradients to land on the Moon 🏆

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 ❤

• Neria Uzan
• Anthony Lapadula

Let's connect!

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