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Hands-On Q-Learning with Python
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4074618Hands-On Q-Learning with Pythonhttps://www.gandhi.com.mx/hands-on-q-learning-with-python-9781789345759/phttps://gandhi.vtexassets.com/arquivos/ids/3711133/e334ed10-8d8a-487f-ae4d-330dfed07273.jpg?v=638385763487500000574604MXNPackt PublishingInStock/Ebooks/<p><strong>Leverage the power of reward-based training for your deep learning models with Python</strong></p><h4>Key Features</h4><ul><li>Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)</li><li>Study practical deep reinforcement learning using Q-Networks</li><li>Explore state-based unsupervised learning for machine learning models</li></ul><h4>Book Description</h4><p>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.</p><p>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 Gyms 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, youll gain a sense of whats in store for reinforcement learning.</p><p>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.</p><h4>What you will learn</h4><ul><li>Explore the fundamentals of reinforcement learning and the state-action-reward process</li><li>Understand Markov decision processes</li><li>Get well versed with libraries such as Keras, and TensorFlow</li><li>Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym</li><li>Choose and optimize a Q-Networks learning parameters and fine-tune its performance</li><li>Discover real-world applications and use cases of Q-learning</li></ul><h4>Who this book is for</h4><p>If 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.</p>...4010123Hands-On Q-Learning with Python574604https://www.gandhi.com.mx/hands-on-q-learning-with-python-9781789345759/phttps://gandhi.vtexassets.com/arquivos/ids/3711133/e334ed10-8d8a-487f-ae4d-330dfed07273.jpg?v=638385763487500000InStockMXN99999DIEbook20199781789345759_W3siaWQiOiJlOTk5NWYxYS0xNTFjLTRkZjYtYThmMy1hOGM4MjhhOTI1ZjAiLCJsaXN0UHJpY2UiOjYwNCwiZGlzY291bnQiOjMwLCJzZWxsaW5nUHJpY2UiOjU3NCwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjUtMDUtMjhUMjE6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781789345759_<p><strong>Leverage the power of reward-based training for your deep learning models with Python</strong></p><h4>Key Features</h4><ul><li>Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)</li><li>Study practical deep reinforcement learning using Q-Networks</li><li>Explore state-based unsupervised learning for machine learning models</li></ul><h4>Book Description</h4><p>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.</p><p>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 Gyms 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, youll gain a sense of whats in store for reinforcement learning.</p><p>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.</p><h4>What you will learn</h4><ul><li>Explore the fundamentals of reinforcement learning and the state-action-reward process</li><li>Understand Markov decision processes</li><li>Get well versed with libraries such as Keras, and TensorFlow</li><li>Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym</li><li>Choose and optimize a Q-Networks learning parameters and fine-tune its performance</li><li>Discover real-world applications and use cases of Q-learning</li></ul><h4>Who this book is for</h4><p>If 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.</p>(*_*)9781789345759_<p><strong>Leverage the power of reward-based training for your deep learning models with Python</strong></p><h4>Key Features</h4><ul><li>Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)</li><li>Study practical deep reinforcement learning using Q-Networks</li><li>Explore state-based unsupervised learning for machine learning models</li></ul><h4>Book Description</h4><p>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.</p><p>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 Gyms 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, youll gain a sense of whats in store for reinforcement learning.</p><p>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.</p><h4>What you will learn</h4><ul><li>Explore the fundamentals of reinforcement learning and the state-action-reward process</li><li>Understand Markov decision processes</li><li>Get well versed with libraries such as Keras, and TensorFlow</li><li>Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym</li><li>Choose and optimize a Q-Networks learning parameters and fine-tune its performance</li><li>Discover real-world applications and use cases of Q-learning</li></ul><h4>Who this book is for</h4><p>If 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.</p>...9781789345759_Packt Publishinglibro_electonico_8a4f3574-0dcd-3f90-b33d-447a3b7a2186_9781789345759;9781789345759_9781789345759Nazia HabibInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/packt-epub-7fed42c5-10d4-4905-a2e5-4e9d7c662a76.epub2019-04-19T00:00:00+00:00Packt Publishing