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.
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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