30 Best Reinforcement Learning Books of All Time
We've researched and ranked the best reinforcement learning books in the world, based on recommendations from world experts, sales data, and millions of reader ratings. Learn more
1
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This... more
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This... more
Recommended by Zachary Lipton, and 1 others. Zachary Lipton@innerproduct 1. Tor Lattimore Great book work on bandits (https://t.co/gttspSm40W) and work on causality + bandits (https://t.co/lkwvtEiKvE) 2. Caroline Uhler — Interesting work on causal inference + discovery, causal inference under measurement error etc (https://t.co/I3IRpwmdMd) (Source)
2
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple...
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Recommended by Mark Tabladillo, and 1 others. Mark TabladilloBook to Start You on Machine Learning - KDnuggets https://t.co/19fdX59b0d This book is “Hands-On Machine Learning with Scikit-Learn & TensorFlow”. each new revision has become an even better version of one of the best in-depth resources to learn Machine Learning by doing. https://t.co/ujyUH3xU3e (Source)
5
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques - especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.
Key features of this revised and improved Second Edition include:
- Extensive coverage, via step-by-step recipes, of powerful new algorithms... more
Key features of this revised and improved Second Edition include:
- Extensive coverage, via step-by-step recipes, of powerful new algorithms... more
7
Generative modeling is one of the hottest topics in artificial intelligence. Recent advances in the field have shown how it's possible to teach a machine to excel at human endeavors--such as drawing, composing music, and completing tasks--by generating an understanding of how its actions affect its environment.
With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You'll also learn how to... more
With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You'll also learn how to... more
8
Ideal for students who are new to cursive writing and those who need a refresher, Cursive Writing: Instruction, Practice, and Reinforcement for fourth to ninth grades provides step-by-step instruction and practice in cursive handwriting. This Cursive Handwriting practice book provides real-world examples that motivate students to perfect their handwriting.
Mark Twain Media Publishing Company specializes in providing engaging supplemental books and decorative resources to complement middle- and upper-grade classrooms. Designed by leading educators, this product line covers a range... more
Mark Twain Media Publishing Company specializes in providing engaging supplemental books and decorative resources to complement middle- and upper-grade classrooms. Designed by leading educators, this product line covers a range... more
10
Neural Networks and Deep Learning is a free online book. The book will teach you about:
* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
* Deep learning, a powerful set of techniques for learning in neural networks
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts... more
* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
* Deep learning, a powerful set of techniques for learning in neural networks
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts... more
Don't have time to read the top Reinforcement Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
12
Artificial Intelligence is one of the most exciting technologies of the century, and Deep Learning is in many ways the “brain” behind some of the world’s smartest Artificial Intelligence systems out there. Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across... more
14
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance.
Many important problems involve decision making under uncertainty--that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making... more
Many important problems involve decision making under uncertainty--that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making... more
15
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.
Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms... more
Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms... more
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The social learning theory of crime integrates Edwin H. Sutherland's diff erential association theory with behavioral learning theory. It is a widely accepted and applied approaches to criminal and deviant behavior. However, it is also widely misinterpreted, misstated, and misapplied.
This is the fi rst single volume, in-depth, authoritative discussion of the background, concepts, development, modifications, and empirical tests of social learning theory. Akers begins with a personal account of Sutherland's involvement in criminology and the origins of his infl uential perspective.... more
This is the fi rst single volume, in-depth, authoritative discussion of the background, concepts, development, modifications, and empirical tests of social learning theory. Akers begins with a personal account of Sutherland's involvement in criminology and the origins of his infl uential perspective.... more
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When looking for ways to improve your website, how do you decide which changes to make? And which changes to keep? This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site. Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success.
This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You’ll quickly learn the... more
This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You’ll quickly learn the... more
18
Now in its fourth edition, Michael Domjan's classic textbook offers an introduction to learning and conditioning in a concise and accessible style, including the latest influential research findings and theoretical perspectives.
Basic principles of learning and conditioning are relevant to an increasingly broad range of psychologists and neuroscientists. Yet in recent years, these core areas have become less prevalent in psychology and neuroscience curricula. As a result, many researchers today lack the training to understand key concepts that underlie human development... more
Basic principles of learning and conditioning are relevant to an increasingly broad range of psychologists and neuroscientists. Yet in recent years, these core areas have become less prevalent in psychology and neuroscience curricula. As a result, many researchers today lack the training to understand key concepts that underlie human development... more
19
School Zone sticker workbooks provide a fun-filled introduction to important readiness skills. Each workbook is carefully designed and reviewed by teachers to promote your child's early learning success. The interesting activities, clear directions, and interactive stickers will provide hours of educational fun for your child.
The Alphabet Fun sticker workbook introduces your child to the alphabet and beginning phonics through playful activities. The interesting lessons focus on recognizing letters, printing letters, identifying beginning sounds, matching pictures to words,... more
The Alphabet Fun sticker workbook introduces your child to the alphabet and beginning phonics through playful activities. The interesting lessons focus on recognizing letters, printing letters, identifying beginning sounds, matching pictures to words,... more
20
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the... more
Don't have time to read the top Reinforcement Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
23
The author and the Seattle Science Notebook Program have outlined the strategies of using science notebooks with a diverse population of students and documented their effectiveness. The thoughtful approach, well explained in the book, keeps the goals of inquiry-based science and writing clearly focused and mutually supportive. - Harold Pratt Former President, National Science Teachers Association This book does more than make a case for science notebooks. It provides specific teaching guidelines, strategies, activities, and rich examples of student work that teachers can use... more
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Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover.
Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks:... more
Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks:... more
27
Learn how to solve challenging machine learning problems with TensorFlow, Google's revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines.
TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground... more
TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground... more
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Build Machine Learning models with a sound statistical understanding. About This Book - Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. - Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. - Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming... more
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Don't have time to read the top Reinforcement Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.