Deep Reinforcement Learning 2.0

Welcome to Deep Reinforcement Learning 2.0!

In this course, we will learn and implement a new incredibly smart AI model, called the Twin–Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q–Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field).

To approach this model the right way, we structured the course in three parts:

Part 1: Fundamentals
In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q–Learning, Deep Q–Learning, Policy Gradient, Actor–Critic and more.

Part 2: The Twin–Delayed DDPG Theory
We will study in depth the whole theory behind the model. You will clearly see the whole construction and training process of the AI through a series of clear visualization slides. Not only will you learn the theory in details, but also you will shape up a strong intuition of how the AI learns and works. The fundamentals in Part 1, combined to the very detailed theory of Part 2, will make this highly advanced model accessible to you, and you will eventually be one of the very few people who can master this model.