Download or read book Reinforcements written by Heidi Grant and published by Harvard Business Press. This book was released on 2018-06-12 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans have a natural instinct to help others. Imagine walking up to a stranger on the subway and asking them for their seat. What about asking a random person on the street if you could borrow their phone? If the idea makes you squeamish, you're not alone--social psychologists have found that doing these very things makes most of us almost unbearably uncomfortable. But here's the funny thing: even though we hate to ask for help, most people are wired to be helpful. And that's a good thing, because every day in the modern, uber-collaborative workplace, we all need to know when and how to call in the cavalry. However, asking people for help isn't intuitive; in fact, a lot of our instincts are wrong. As a result, we do a poor job of calling in the reinforcements we need, leaving confused or even offended colleagues in our wake. This pragmatic book explains how to get it right. With humor, insight, and engaging storytelling, Heidi Grant, PhD, describes how to elicit helpful behavior from your friends, family, and colleagues--in a way that leaves them feeling genuinely happy to lend a hand. Whether you're a first-time manager or a seasoned leader, getting people to pitch in is what leadership is. Fortunately, people have a natural instinct to help other human beings; you just need to know how to channel this urge into what it is you specifically need them to do. It's not manipulation. It's just management.
Download or read book What Drives Winning written by and published by . This book was released on 2016-04-30 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Reinforcement and Behavior written by Jack T. Tapp and published by Academic Press. This book was released on 2013-09-11 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement and Behavior brings together research findings and views of a number of investigators on the principles of learning and reinforcement. Their work has challenged the more traditional interpretations of the nature of the reinforcement process. Within the book, the chapters are organized from a molar level of analysis to a molecular one, not only to reflect the diversity of strategies that are being brought to bear on the problem, but also to show that the research on the nature of reinforcement transcends lines of scientific disciplines and that many different levels of analysis contribute to our understanding of the phenomenon. The first and last chapters give historical perspective to the remainder of the book by reviewing the contributions of a number of individuals who have dealt with the problem in their own work and by pointing out some of the major issues on the molar level that are still unresolved. The remaining chapters can be roughly divided into two categories. One examines the consequences of rewards on behavior in order to specify the limits of their operations and the variables which predispose organisms to be responsive to the consequences of rewards. The other deals with the neural mechanisms which underlie reinforcement and learning.
Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Download or read book Reinforcement written by R. M. Gilbert and published by Academic Press. This book was released on 2014-05-10 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement: Behavioral Analyses covers the proceedings of the 1970 Symposium on Schedule-induced and Schedule-Dependent Phenomena, held in Toronto, Ontario, Canada. This symposium highlights theoretically inclined papers on reinforcement processes. This text contains 10 chapters and begins with a description of how behavior is induced by various environmental events, especially reinforcing events, as well as the relationship between control by inducing stimuli and reinforceability. The subsequent chapters deal with reinforcement phenomena in terms of preference relations and the conditioned emotional responses in terms of opposing motivational processes. These topics are followed by reviews of schedule-dependent effects of preaversive stimuli and the maintenance of behavior by apparent reinforcers that might be expected to punish, as well as the identification of critical variable that underlie the phenomenon. Other chapters examine the interactions between operant and responded conditioning processes. The final chapters outline the experiments on behavior stream whose hallmark is reinforcement if the absence of specified behavior. These chapters emphasize the analogy between the evolution of species and the modification of behavior. This book will be of value to behaviorists and psychologists.
Download or read book Motivated Reinforcement Learning written by Kathryn E. Merrick and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated learning is an emerging research field in artificial intelligence and cognitive modelling. Computational models of motivation extend reinforcement learning to adaptive, multitask learning in complex, dynamic environments – the goal being to understand how machines can develop new skills and achieve goals that were not predefined by human engineers. In particular, this book describes how motivated reinforcement learning agents can be used in computer games for the design of non-player characters that can adapt their behaviour in response to unexpected changes in their environment. This book covers the design, application and evaluation of computational models of motivation in reinforcement learning. The authors start with overviews of motivation and reinforcement learning, then describe models for motivated reinforcement learning. The performance of these models is demonstrated by applications in simulated game scenarios and a live, open-ended virtual world. Researchers in artificial intelligence, machine learning and artificial life will benefit from this book, as will practitioners working on complex, dynamic systems – in particular multiuser, online games.
Download or read book Training Reinforcement written by Anthonie Wurth and published by John Wiley & Sons. This book was released on 2018-06-21 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: A proven framework to fill the gap between "knowing" and "doing" Training Reinforcement offers expert guidance for more effective training outcomes. Last year, US companies spent over $165 Billion on training; while many training programs themselves provide valuable skills and concepts, even the best-designed programs are ineffective because the learned behaviors are not reinforced. Without reinforcement, learned information gets shuffled to the back of the mind in the "nice to know" file, never again to see the light of day. This book bridges the canyon between learning and doing by providing solid reinforcement strategies. Written by a former Olympic athlete and corporate training guru, this methodology works with human behavior rather than against it; you'll learn where traditional training methods fail, and how to fill those gaps with proven techniques that help training "stick." There's a difference between "telling" and "teaching," and that difference is reinforcement. Learned skills and behaviors cannot be truly effective until they are engrained, and they can only become engrained through use, encouragement, and measureable progress. This book provides a robust reinforcement framework that adds long-term value to any training program. Close the 5 Reinforcement Gaps and master the 3 Phases for results Create friction and direction while providing the perfect Push-Pull Follow the Reinforcement Flow to maintain consistency and effectiveness Create measureable behavior change by placing the participant central to the process Reinforcing training means more than simple repetition and reminders, and effective reinforcement requires a careful balance of independence and oversight. Training Reinforcement provides a ready-made blueprint with proven results, giving trainers and managers an invaluable resource for leading behavioral change.
Download or read book Theories of Learning written by Ernest Ropiequet Hilgard and published by . This book was released on 1956 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: The nature of learning theories; Thorndike's connectionism; Guthrie's contiguous conditioning; Skinner's operant conditioning; Hull's systematic behavior theory; Tolman's sign learning; Classical gestalt theory; Lewin's field theory; Freud's psychodynamics; Functionalism; The emergence of mathematical models; Current developments: I. Reinforcement and drive; Current developments: II. Some persisting issues; Retrospect and prospect.
Download or read book Drives and Reinforcements written by James Olds and published by . This book was released on 1977 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Learning and Memory written by W. Scott Terry and published by Routledge. This book was released on 2017-10-16 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thoroughly updated edition provides a balanced review of the core methods and the latest research on animal learning and human memory. The relevance of basic principles is highlighted throughout via everyday examples to ignite student interest, along with more traditional examples from human and animal laboratory studies. Individual differences in age, gender, learning style, cultural background, or special abilities (such as the math gifted) are highlighted within each chapter to help students see how the principles may be generalized to other subject populations. The basic processes of learning – such as classical and instrumental conditioning and encoding and storage in long-term memory in addition to implicit memory, spatial learning, and remembering in the world outside the laboratory – are reviewed. The general rules of learning are described along with the exceptions, limitations, and best applications of these rules. The relationship between the fields of neuropsychology and learning and memory is stressed throughout. The relevance of this research to other disciplines is reflected in the tone of the writing and is demonstrated through a variety of examples from education, neuropsychology, rehabilitation, psychiatry, nursing and medicine, I/O and consumer psychology, and animal behavior. Each chapter begins with an outline and concludes with a detailed summary. A website for instructors and students accompanies the book. Updated throughout with new research findings and examples the new edition features: A streamlined presentation for today’s busy students. As in the past, the author supports each concept with a research example and real-life application, but the duplicate example or application now appears on the website so instructors can use the additional material to illustrate the concepts in class. Expanded coverage of neuroscience that reflects the current research of the field including aversive conditioning (Ch. 5) and animal working memory (Ch. 8). More examples of research on student learning that use the same variables discussed in the chapter, but applies them in a classroom or student’s study environment. This includes research that applies encoding techniques to student learning, for example: studying: recommendations from experts (Ch. 1); the benefits of testing (Ch. 9); and Joshua Foer’s Moonwalking with Einstein, on his quest to become a memory expert (Ch. 6). More coverage of unconscious learning and knowledge (Ch. 11). Increased coverage of reinforcement and addiction (Ch. 4), causal and language learning (Ch. 6), working memory (WM) and the effects of training on WM, and the comparative evolution of WM in different species (Ch. 8), and genetics and learning (Ch. 12).
Download or read book Reinforcement Learning with TensorFlow written by Sayon Dutta and published by Packt Publishing Ltd. This book was released on 2018-04-24 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of the Reinforcement Learning techniques to develop self-learning systems using Tensorflow Key Features Learn reinforcement learning concepts and their implementation using TensorFlow Discover different problem-solving methods for Reinforcement Learning Apply reinforcement learning for autonomous driving cars, robobrokers, and more Book Description Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym. What you will learn Implement state-of-the-art Reinforcement Learning algorithms from the basics Discover various techniques of Reinforcement Learning such as MDP, Q Learning and more Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym Understand how Reinforcement Learning Applications are used in robotics Who this book is for If you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required.
Download or read book The Great Mental Models Volume 1 written by Shane Parrish and published by Penguin. This book was released on 2024-10-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage.
Download or read book Performance and Reinforcement as Functions of Drive and Drive Shifts written by Joseph Culver Hammock and published by . This book was released on 1953 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Rest Principle written by John David Sinclair and published by Psychology Press. This book was released on 2014-04-04 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: First published in 1982. The human brain is the most complex object on Earth that can be studied scientifically: a collection of over 100 billion neurons squeezed into a space about the size of a grapefruit, which somehow is able to control all that you feel, do, and know. There still is little understanding of the most important and interesting functions of the brain, such as what really happens up there when you learn something, when you are thinking, or when you are feeling happy. In this book the author attempts to organize nearly the entire field of psychology within a single new theory, based upon only one very simple assumption about neuronal functioning.
Download or read book Reinforcement Learning Enabled Intelligent Energy Management for Hybrid Electric Vehicles written by Teng Liu and published by Springer Nature. This book was released on 2022-06-01 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application. In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
Download or read book The The Reinforcement Learning Workshop written by Alessandro Palmas and published by Packt Publishing Ltd. This book was released on 2020-08-18 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.
Download or read book Reinforcement Learning for Sequential Decision and Optimal Control written by Shengbo Eben Li and published by Springer Nature. This book was released on 2023-04-05 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, including chess games, computer games and robotic control. RL is also considered to be a promising and powerful tool to create general artificial intelligence in the future. As an interdisciplinary field of trial-and-error learning and optimal control, RL resembles how humans reinforce their intelligence by interacting with the environment and provides a principled solution for sequential decision making and optimal control in large-scale and complex problems. Since RL contains a wide range of new concepts and theories, scholars may be plagued by a number of questions: What is the inherent mechanism of reinforcement learning? What is the internal connection between RL and optimal control? How has RL evolved in the past few decades, and what are the milestones? How do we choose and implement practical and effective RL algorithms for real-world scenarios? What are the key challenges that RL faces today, and how can we solve them? What is the current trend of RL research? You can find answers to all those questions in this book. The purpose of the book is to help researchers and practitioners take a comprehensive view of RL and understand the in-depth connection between RL and optimal control. The book includes not only systematic and thorough explanations of theoretical basics but also methodical guidance of practical algorithm implementations. The book intends to provide a comprehensive coverage of both classic theories and recent achievements, and the content is carefully and logically organized, including basic topics such as the main concepts and terminologies of RL, Markov decision process (MDP), Bellman’s optimality condition, Monte Carlo learning, temporal difference learning, stochastic dynamic programming, function approximation, policy gradient methods, approximate dynamic programming, and deep RL, as well as the latest advances in action and state constraints, safety guarantee, reference harmonization, robust RL, partially observable MDP, multiagent RL, inverse RL, offline RL, and so on.
