646310Deep Reinforcement Learning Hands-Onhttps://www.gandhi.com.mx/deep-reinforcement-learning-hands-on-1/phttps://gandhi.vtexassets.com/arquivos/ids/1395922/eec4eb82-7cde-475d-80f9-b427680fe4a0.jpg?v=63868520406477000011821313MXNPackt PublishingInStock/Ebooks/645834Deep Reinforcement Learning Hands-On11821313https://www.gandhi.com.mx/deep-reinforcement-learning-hands-on-1/phttps://gandhi.vtexassets.com/arquivos/ids/1395922/eec4eb82-7cde-475d-80f9-b427680fe4a0.jpg?v=638685204064770000InStockMXN99999DIEbook20209781838820046_W3siaWQiOiJlZDkzZmM0OS1iMWFiLTRmN2MtODJmNC01MTFiY2M4YWViM2IiLCJsaXN0UHJpY2UiOjEzMTMsImRpc2NvdW50IjoxMzEsInNlbGxpbmdQcmljZSI6MTE4MiwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMTEtMjhUMTA6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781838820046_<p><strong>New edition of the bestselling guide to deep reinforcement learning and how its used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more</strong></p><h4>Key Features</h4><ul><li>Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters</li><li>Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods</li><li>Apply RL methods to cheap hardware robotics platforms</li></ul><h4>Book Description</h4><p>Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.</p><p>With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubiks Cube), multi-agent methods, Microsofts TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.</p><p>In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than 100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.</p><p>In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.</p><h4>What you will learn</h4><ul><li>Understand the deep learning context of RL and implement complex deep learning models</li><li>Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others</li><li>Build a practical hardware robot trained with RL methods for less than 100</li><li>Discover Microsofts TextWorld environment, which is an interactive fiction games platform</li><li>Use discrete optimization in RL to solve a Rubiks Cube</li><li>Teach your agent to play Connect 4 using AlphaGo Zero</li><li>Explore the very latest deep RL research on topics including AI chatbots</li><li>Discover advanced exploration techniques, including noisy networks and network distillation techniques</li></ul><h4>Who this book is for</h4><p>Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL</p>(*_*)9781838820046_<p><strong>New edition of the bestselling guide to deep reinforcement learning and how its used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more</strong></p><h4>Key Features</h4><ul><li>Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters</li><li>Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods</li><li>Apply RL methods to cheap hardware robotics platforms</li></ul><h4>Book Description</h4><p>Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.</p><p>With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubiks Cube), multi-agent methods, Microsofts TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.</p><p>In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than 100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.</p><p>In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.</p><h4>What you will learn</h4><ul><li>Understand the deep learning context of RL and implement complex deep learning models</li><li>Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others</li><li>Build a practical hardware robot trained with RL methods for less than 100</li><li>Discover Microsofts TextWorld environment, which is an interactive fiction games platform</li><li>Use discrete optimization in RL to solve a Rubiks Cube</li><li>Teach your agent to play Connect 4 using AlphaGo Zero</li><li>Explore the very latest deep RL research on topics including AI chatbots</li><li>Discover advanced exploration techniques, including noisy networks and network distillation techniques</li></ul><h4>Who this book is for</h4><p>Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL</p>...(*_*)9781838820046_<p><b>Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more</b></p><h2>Key Features</h2><ul><li>Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters</li><li>Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods</li><li>Apply RL methods to cheap hardware robotics platforms</li></ul><h2>Book Description</h2>Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubiks Cube), multi-agent methods, Microsofts TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than 100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.<h2>What you will learn</h2><ul><li>Understand the deep learning context of RL and implement complex deep learning models</li><li>Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others</li><li>Build a practical hardware robot trained with RL methods for less than 100</li><li>Discover Microsoft s TextWorld environment, which is an interactive fiction games platform</li><li>Use discrete optimization in RL to solve a Rubik s Cube</li><li>Teach your agent to play Connect 4 using AlphaGo Zero</li><li>Explore the very latest deep RL research on topics including AI chatbots</li><li>Discover advanced exploration techniques, including noisy networks and network distillation techniques</li></ul><h2>Who this book is for</h2><p>Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL</p>...9781838820046_Packt Publishinglibro_electonico_93d06205-a929-3e39-8402-7c737ab8e859_9781838820046;9781838820046_9781838820046Maxim LapanInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/packt-epub-5883aace-92d9-4c69-bff4-b68c78e6496a.epub2020-01-31T00:00:00+00:00Packt Publishing