Hands-On Artificial Intelligence for IoT

DQN to play an Atari game

The DQN we'll learn here is based on a DeepMind paper (https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf). At the heart of DQN is a deep convolutional neural network that takes as input the raw pixels of the game environment (just like any human player would see), captured one screen at a time, and as output, returns the value for each possible action. The action with the maximum value is the chosen action:

1. The first step is to get all of the modules we'll need:

import gymimport sysimport randomimport numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltfrom datetime import datetimefrom scipy.misc import imresize

2. We chose the Breakout game from the list of OpenAI ...