{"id":11257,"date":"2024-01-02T02:39:30","date_gmt":"2024-01-02T02:39:30","guid":{"rendered":"https:\/\/lacountystore.com\/\/?p=11257"},"modified":"2024-01-02T02:39:30","modified_gmt":"2024-01-02T02:39:30","slug":"best-book-on-reinforcement-learning","status":"publish","type":"post","link":"https:\/\/lacountystore.com\/best-book-on-reinforcement-learning\/","title":{"rendered":"20 Best Book On Reinforcement Learning (2024 Update)"},"content":{"rendered":"

Are you looking for the Best Book On Reinforcement Learning<\/strong>? If so, you’ve come to the right place.<\/p>\n

Choosing the Best Book On Reinforcement Learning can be difficult as there are so many considerations, such as Crayola, Elsevier, Hasbro, LEGO, Nintendo, Penguin Random House, TopQ, Amazon.com. We have done a lot of research to find the Top 20 Best Book On Reinforcement Learning<\/strong> available.<\/p>\n

The average cost is $65.82<\/i>. Sold comparable range in price from a low of $8.88<\/i> to a high of $293.93<\/i>.<\/p>\n

Based on the research we did, we think Reinforcement Learning: An Introduction [Book]<\/strong><\/a> is the best overall. Read on for the rest of the great options and our buying guide, where you can find all the information you need to know before making an informed purchase.<\/p>\n

20 Best Book On Reinforcement Learning (18 Sellers)<\/h2>\n
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Product Image<\/span><\/th>\nProduct Name<\/span><\/th>\nFeatures<\/span><\/th>\nCheck Price<\/span><\/th>\n<\/tr>\n<\/thead>\n
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  • \n
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    • Binding type: hardback<\/li>\n
    • Year published: 20181113<\/li>\n
    • Number of pages: 552<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Binding type: paperback<\/li>\n
    • Publisher: springer verlag, singapore<\/li>\n
    • Year published: 2022-06-12<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Focus on the foundational theory underpinning reinforcement learning and software design of the corresponding models and algorithms.<\/li>\n
    • Suitable for a professional audience of quantitative analysts or data scientists.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Key features.<\/li>\n
    • Structuring problems as markov decision processes.<\/li>\n
    • Popular algorithms such deep q-networks, policy gradient method and evolutionary algorithms and the intuitions that drive them.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Binding type: paperback<\/li>\n
    • Year published: 2020-11-10<\/li>\n
    • Number of pages: 465<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Builds a story about the power of teachers, feedback, and a model of learning and understanding.<\/li>\n
    • This book covers areas such as the influence of the student, home, school, curricula, teacher, and teaching strategies.<\/li>\n
    • It develops a model of teaching and learning based on the notion of visible teaching and visible learning.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Can i set limits and still be a loving person<\/li>\n
    • What are legitimate boundaries<\/li>\n
    • How do i effectively manage my digital life so that it doesn&#39;t control me<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Grand central pub<\/li>\n
    • Brand: grand central publishing<\/li>\n
    • Easy to understand<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Learn what rl is and how the algorithms help solve problems<\/li>\n
    • Become grounded in rl fundamentals including markov decision processes, dynamic programming, and temporal difference learning<\/li>\n
    • Dive deep into a range of value and policy gradient methods<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Binding type: paperback<\/li>\n
    • Year published: 2022-07-26<\/li>\n
    • Number of pages: 568<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Why sometimes letting your mind wander is an important part of the learning process.<\/li>\n
    • How to avoid rut think in order to think outside the box.<\/li>\n
    • Why having a poor memory can be a good thing.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Languages: english<\/li>\n
    • Product format: hardback<\/li>\n
    • Publisher: random house usa<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Library journal (starred review) everyone should read this book.<\/li>\n
    • Chip heath and dan heath, authors of made to stick one of the most influential books ever about motivation.<\/li>\n
    • Po bronson, author of nurtureshock if you manage people or are a parent (which is a form of managing people), drop everything and read mindset.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • The book might be ex-library copy and may have the markings and stickers associated from the library.<\/li>\n
    • The book may have considerable highlights\/notes\/underlined pages but the text is legible.<\/li>\n
    • Safe and secure mailer.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Power of habits<\/li>\n
    • Brand: random house trade<\/li>\n
    • Can be considered as a gifting option\/ perfect for gifting purpose<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Trial-and-error search and delayed reward.<\/li>\n
    • Are the most important distinguishing features of reinforcement learning.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Trial-and-error search and delayed reward.<\/li>\n
    • Are the most important distinguishing features of reinforcement learning.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Series: foundations and trends in machine learning<\/li>\n
    • Binding type: paperback<\/li>\n
    • Publisher: now publishers inc<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • After completing this book, you will understand reinforcement learning with deep q and generative adversarial networks using the tensorflow library.<\/li>\n
    • What you will learn.<\/li>\n
    • Understand the fundamentals of reinforcement learning.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • After completing this book, you will understand reinforcement learning with deep q and generative adversarial networks using the tensorflow library.<\/li>\n
    • What you will learn.<\/li>\n
    • Understand the fundamentals of reinforcement learning.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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