Tutorial: Double Deep Q-Learning with Dueling Network Architecture

If you are as fascinated by Deep Q-Learning as I am but never had the time to understand or implement it, this is for you: In one Jupyter notebook I will 1) briefly explain how Reinforcement Learning differs from Supervised Learning, 2) discuss the theory behind Deep Q-Networks (DQN) by telling you where you find the respective explanations in the papers and what they mean and 3) how to implement the components needed to make it work in python and tensorflow. In 2013 a London based startup called DeepMind published a groundbreaking paper called Playing Atari with Deep Reinforcement Learning on arXiv: The authors presented a variant of Reinforcement Learning called Deep Q-Learning that is able to successfully learn control policies for different Atari 2600 games receiving only screen pixels as input and a reward when the game score changes. This is an astonishing result because previously "AIs" used to be limited to one single game, for instance, chess, whereas in this case the types and contents of the games in the Arcade Learning Environment vary significantly and yet no adjustment of the architecture, learning algorithm or hyperparameters is needed. No wonder DeepMind was bought by Google for 500 Million Dollars. The company has since been one of the leading institutions advancing Deep Learning research and a later article discussing DQN has been published in Nature.