Deep Reinforcement Learning (RL) Using Python

Explanation of the game rules	The game played by a human

In this tutorial series, we are going through every step of building an expert Reinforcement Learning (RL) agent that is capable of playing games.

This series is divided into three parts:

• Part 1: Designing and Building the Game Environment. In this part we will build a game environment and customize it to make the RL agent able to train on it.

• Part 2: Build and Train the Deep Q Neural Network (DQN). In this part, we define and build the different layers of DQN and train it.

• Part 3: Test and Play the Game.

We might also try making another simple game environment and use Q-Learning to create an agent that can play this simple game.

The Motivation:

One time I was in the rabbit hole of YouTube and THIS VIDEO was recommended to me, it was about the **sense of self in human babies, after watching the video a similar question popped into my mind “Can I develop a smart agent that is smart enough to have a sense of its body and has the ability to change its features to accomplish a certain task?”

This series is my way of answering this question.