{"product_id":"9781617295454","title":"Grokking Deep Reinforcement Learning","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eMorales, Miguel\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e11\/10\/2020\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003e\u003ci\u003eGrokking Deep Reinforcement Learning\u003c\/i\u003e uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eSummary\u003c\/b\u003e\u003cbr\u003e We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. \u003ci\u003eGrokking Deep Reinforcement Learning\u003c\/i\u003e introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.\u003cbr\u003e \u003cbr\u003e Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e We learn by interacting with our environment, and the rewards or punishments we experience guide our future behavior. Deep reinforcement learning brings that same natural process to artificial intelligence, analyzing results to uncover the most efficient ways forward. DRL agents can improve marketing campaigns, predict stock performance, and beat grand masters in Go and chess.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e\u003cbr\u003e     An introduction to reinforcement learning\u003cbr\u003e     DRL agents with human-like behaviors\u003cbr\u003e     Applying DRL to complex situations\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e For developers with basic deep learning experience.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e Miguel Morales works on reinforcement learning at Lockheed Martin and is an instructor for the Georgia Institute of Technology’s Reinforcement Learning and Decision Making course.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 1 Introduction to deep reinforcement learning\u003cbr\u003e \u003cbr\u003e 2 Mathematical foundations of reinforcement learning\u003cbr\u003e \u003cbr\u003e 3 Balancing immediate and long-term goals\u003cbr\u003e \u003cbr\u003e 4 Balancing the gathering and use of information\u003cbr\u003e \u003cbr\u003e 5 Evaluating agents’ behaviors\u003cbr\u003e \u003cbr\u003e 6 Improving agents’ behaviors\u003cbr\u003e \u003cbr\u003e 7 Achieving goals more effectively and efficiently\u003cbr\u003e \u003cbr\u003e 8 Introduction to value-based deep reinforcement learning\u003cbr\u003e \u003cbr\u003e 9 More stable value-based methods\u003cbr\u003e \u003cbr\u003e 10 Sample-efficient value-based methods\u003cbr\u003e \u003cbr\u003e 11 Policy-gradient and actor-critic methods\u003cbr\u003e \u003cbr\u003e 12 Advanced actor-critic methods\u003cbr\u003e \u003cbr\u003e 13 Toward artificial general intelligence","brand":"Manning","offers":[{"title":"Default Title","offer_id":44233951772927,"sku":"9781617295454","price":49.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_18f872e8-0109-49af-899b-7e596144686c.jpg?v=1771352086","url":"https:\/\/massivebookshop.com\/products\/9781617295454","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}