Welcome to Part 1!

In this part we are going get started into the World of AI and build our own self driving car. This is going to be a modelled version of a car (so it won't be driving on the streets of real cities) but still - it will learn how to drive itself. And the key word here is learn, because the car will not be given any rules on how to operate in the environment before hand - it will have to figure everything out on it's own. And to achieve that we will be using Deep Q-Learning.

Deep Q-Learning is the result of combining Q-Learning with an Artificial Neural Network. The states of the environment are encoded by a vector which is passed as input into the Neural Network. Then the Neural Network will try to predict which action should be played, by returning as outputs a Q-value for each of the possible actions. Eventually, the best action to play is chosen by either taking the one that has the highest Q-value, or by overlaying a Softmax function. 

In this first Part, we will implement a Deep Q-Learning model to build an AI for a Self Driving Car. 

See you inside!

Kirill & Hadelin