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. In this reinforcement learning course, I will teach you how.
Welcome to the Hands-on reinforcement learning course ❤️
Let’s walk this beautiful path from the fundamentals to cutting edge deep reinforcement learning, together!
👉🏽From zero to HERO 🦸🏻🦸🏼🦸🏽🦸🏾🦸🏿🦸♂️🦸♀️
👉🏽Step by step
👉🏽Clean Python code
👉🏽Intuitions, tips and tricks explained.
All the reinforcement learning course code is in this Github repo. Don’t forget to give it a ⭐!
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!
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?
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.
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 🌗.
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