Book Image

Hands-On Q-Learning with Python

By : Nazia Habib
Book Image

Hands-On Q-Learning with Python

By: Nazia Habib

Overview of this book

Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you become familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in scientific research. Toward the end, you’ll gain insight into what’s in store for reinforcement learning. By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.
Table of Contents (14 chapters)
Free Chapter
1
Section 1: Q-Learning: A Roadmap
6
Section 2: Building and Optimizing Q-Learning Agents
9
Section 3: Advanced Q-Learning Challenges with Keras, TensorFlow, and OpenAI Gym

Building your first Q-network

We are using the TensorFlow framework to build a Q-network that will solve the Taxi-v2 task. Note that this is a single-layer network, so it does not qualify as a deep Q-network. We'll be building a deep Q-network implementation in the next chapter.

Many people use the term "deep learning" in association with any machine learning model that uses neural networks, and, in fact, some incorrectly generalize the term "deep Q-network" to include any Q-learning implementation that uses a neural network. The main distinction is that deep learning structures contain many hierarchical neural network layers that are constructed into various architectures.

The primary difference here from the model-free version that we built in Chapter 4, Teaching a Smartcab to Drive Using Q-Learning, is that, instead of updating a Q-table with the exact...