# The Hands-on Reinforcement Learning Course

Reinforcement Learning (RL) is the kind of machine learning closest to how humans and animals learn. It offers us a path towards building general AI systems that can tackle the most complex problems we can think of.

## 1. Introduction to Reinforcement Learning

This first part covers the bare minimum concept and theory you need to embark on this journey, with practical examples and the first code snippet!

## 2. Q-learning to drive a taxi

Q learning is a classical RL algorithm published in the 90s. In this first lesson, we use tabular Q-learning to train a smart taxi driver. Ready to drive?

## 3. SARSA to beat gravity

The Mountain Car problem is an environment where gravity exists (what a surprise) and the goal is to help a poor car win the battle against it.

SARSA is a classical online algorithm that solve this problem like a charm.

## 4. Parametric Q-learning to keep the balance (1/3)

Parametric Q learning combines the strengths of classical Q-learning with modern optimization techniques from Supervised Machine Learning.

## 5. Deep Q learning to keep the balance (2/3)

Let’s replace the linear model from the previous lesson with a deep neural network. And kick-ass solve the Cart Pole environment.

## 6. Hyper-parameters in Deep RL

Hyperparameters in Deep RL are critical to training successful agents. In today’s lesson, we will learn how to find the ones that make you a happy Deep RL developer.

## Deep Learning: Faster, Better, And Free In 3 Easy Steps

Tired of training deep learning models on your laptop, at the speed of… a turtle? 🐢 Not enthusiastic about buying an expensive GPU or optimizing cloud services bills? 💸 Wish there was a better way to do it?

Luckily for you, the answer to the last question is yes.

## 7. Policy Gradients to get to the Moon

**Policy gradients** are a family of powerful reinforcement learning algorithms that can solve complex control tasks. In today’s lesson, we will implement vanilla policy gradients from scratch and **land on the Moon** 🌗.