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Brushing Up on Reinforcement Learning Concepts

In this book, you will learn the fundamentals of Q-learning, a branch of reinforcement learning (RL), and how to apply them to challenging real-world optimization problems. You'll design software that dynamically writes itself, modifies itself, and improves its own performance in real time.

In doing so, you will build self-learning intelligent agents that start with no knowledge of how to solve a problem and independently find optimal solutions to that problem through observation, trial and error, and memory.

RL is one of the most exciting branches of artificial intelligence (AI) and powers some of its most visible successes, from recommendation systems that learn from user behavior to game-playing machines that can beat any human being at chess or Go.

Q-learning is one of the easiest versions of RL to get started with, and...

Key benefits

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

Description

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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Hands-On Deep Learning Architectures with Python

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Total € 92.97

SSV Jul 18, 2019

I was sent a copy of this book by the publisher to read and review.If you are an intermediate level python user and if you are passionate about artificial intelligence and neural networks and are looking to improve your programming skills with Python, then this book is a must purchase!Author Ms. Nazia Habib has created an outstanding textbook that is perfect for self-directed learning. It first begins with an extremely thorough and easy to understand explanation of theoretical concepts surrounding reinforcement learning, and provides extensive information on the coding process with Q learning, using easy to follow examples as well as companion coding exercises to help you integrate your newfound knowledge as you progress through the book.As your skills progress throughout the book, more complex examples including neural networks are introduced, with applications being endless!So if you want to start building your expertise in programming for artificial intelligence, then this book is a must-read!

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Dr. Mark Potter May 15, 2020

Limited in scope, not a great read.

roman575 Jun 30, 2020

Introduction, repetitions, conclusions, summaries, installation instructions comprise 90% of the book. Essential material is very basic and could be find in any 20 pages blog

Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow

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Hands-On Q-Learning with Python

Brushing Up on Reinforcement Learning Concepts

What is RL?

States, actions, and rewards

Key concepts in RL

Value-based versus policy-based iteration

SARSA versus Q-learning – on-policy or off?

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