Hands-On Q-Learning with Python

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.

Type
ebook
Category
Data
publication date
2019-04-19
what you will learn

Explore the fundamentals of reinforcement learning and the state-action-reward process
Understand Markov Decision Processes
Get well-versed with libraries such as Keras, and TensorFlow
Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym
Choose and optimize a Q-network’s learning parameters and fine-tune its performance
Discover real-world applications and use cases of Q-learning

no of pages
212
duration
424
key features
Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP) * Study practical deep reinforcement learning using Q-Networks * Explore state-based unsupervised learning for machine learning models
approach
This book will be an end-to-end guide in learning the states and agents in Q learning for AI, building the Q-networks and tackling any problem with Q learning in building AI applications with popular Python machine learning libraries.
audience
If you are a machine learning developer, engineer, or professional who wants to explore the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.
meta description
Leverage the power of reward-based training for your deep learning models with Python
short description
Q-learning is the reinforcement learning approach behind Deep-Q-Learning and is a values-based learning algorithm in RL. This book will help you get comfortable with developing the effective agents for Q learning and also make you learn to effectively develop and deploy Deep Q networks for complex AI applications.
subtitle
Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
keywords
Reinforcement learning, TensorFlow, machine learning, deep learning, q learning, markov decision process, deep neural networks, Open AI gym
Product ISBN
9781789345803