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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Hands-On Q-Learning with Python - Practical Q-learning with OpenAI Gym, Keras, and TensorFlow (Paperback)
Loot Price: R1,062
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Hands-On Q-Learning with Python - Practical Q-learning with OpenAI Gym, Keras, and TensorFlow (Paperback)
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Leverage the power of reward-based training for your deep learning
models with Python 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 Book
DescriptionQ-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 get familiar with OpenAI Gym
as well as libraries such as Keras and TensorFlow. A few chapters
into the book, you will gain insights into modelfree 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
real-world applications such as scientific research. Toward the
end, you'll gain a sense of 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 Who this book is forIf you are a machine learning
developer, engineer, or professional who wants to delve into 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.
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