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:
- π easy
- ππ medium
- πππ 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.
- Introduction to Reinforcement Learning
- Q-learning to drive a taxi π
- SARSA to beat gravity π
- Parametric Q learning to keep the balance π π
- Policy gradients to land on the Moon π
There are 2 things you can do to contribute to this course:
-
Open a pull request to fix a bug or improve the code readability.
Special thanks to all the students who contributed with valuable feedback and pull requests β€
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