Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Implementing a deep Q-learning algorithm

Another popular method for learning is Q-learning. In Q-learning, we don't focus on mapping an observation to a specific action, but we try to assign some value to the current state (of observations) and act based on that value. The states and can be seen as a Markov decision process, where the environment is stochastic. In a Markov process, the next state only depends on the current state and the following action. So, we assume that all previous states (and actions) are irrelevant.

The Q in Q-learning stands for quality; the function Q(s, a) provides a quality score for action a in state s. The function can be of any type. In a simple form, it can be a lookup table. However, in a more complex environment, this won't work and that's where deep learning comes in place. In the following recipe, we will implement a deep Q-learning algorithm to play Breakout from OpenAI.

Getting ready

Before start implementing the recipe, make sure the OpenAI Gym environment...