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The Power Of Reinforcement
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Book Synopsis The Power of Reinforcement by : Stephen Ray Flora
Download or read book The Power of Reinforcement written by Stephen Ray Flora and published by State University of New York Press. This book was released on 2012-02-01 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: 2004 CHOICE Outstanding Academic Title According to Stephen Ray Flora, reinforcement is a very powerful tool for improving the human condition despite often being dismissed as regarding people as less than human and as "overly simplistic." This book addresses and defends the use of reinforcement principles against a wide variety of attacks. Countering the myths, criticisms, and misrepresentations of reinforcement, including false claims that reinforcement is "rat psychology," the author shows that building reinforcement theory on basic laboratory research is a strength, not a weakness, and allows unlimited applications to human situations as it promotes well-being and productivity. Also examined are reinforcement contingencies, planned or accidental, as they shape behavioral patterns and repertoires in a positive way.
Book Synopsis The Power of Positive Reinforcement by : Judith E. Favell
Download or read book The Power of Positive Reinforcement written by Judith E. Favell and published by Charles C. Thomas Publisher. This book was released on 1977 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis From Bud to Boss by : Kevin Eikenberry
Download or read book From Bud to Boss written by Kevin Eikenberry and published by John Wiley & Sons. This book was released on 2011-01-07 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical advice for making the shift to your first leadership position The number of people who will become first-time supervisors will likely grow in the next 10 years, as Baby Boomers retire. Perhaps the most challenging leadership experience anyone will face isn't one at the top, but their first promotion to leadership. They must deal with the change and uncertainty that comes with a new job, requiring new skills, and they've been promoted from peer to leader. While the book addresses the needs of any manager, supervisor, or leader, it pulls from the best leadership and management thinking, and puts the focus on the difficulties that new leaders experience. Includes practical information for new managers who must supervise friends and former peers Authors are expert consultants who work with leaders at all levels Shows how to adopt the mindset of a leader, including: communicating change, giving feedback, coaching employees, leading productive teams, and achieving goals This much-needed book can help new leaders get beyond the stress and fear to focus on becoming the most effective leader they can be-starting right now.
Book Synopsis The Great Mental Models, Volume 1 by : Shane Parrish
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 209 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.
Book Synopsis Schedules of Reinforcement by : B. F. Skinner
Download or read book Schedules of Reinforcement written by B. F. Skinner and published by B. F. Skinner Foundation. This book was released on 2015-05-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contingent relationship between actions and their consequences lies at the heart of Skinner’s experimental analysis of behavior. Particular patterns of behavior emerge depending upon the contingencies established. Ferster and Skinner examined the effects of different schedules of reinforcement on behavior. An extraordinary work, Schedules of Reinforcement represents over 70,000 hours of research primarily with pigeons, though the principles have now been experimentally verified with many species including human beings. At first glance, the book appears to be an atlas of schedules. And so it is, the most exhaustive in existence. But it is also a reminder of the power of describing and explaining behavior through an analysis of measurable and manipulative behavior-environment relations without appealing to physiological mechanisms in the brain. As en exemplar and source for the further study of behavioral phenomena, the book illustrates the scientific philosophy that Skinner and Ferster adopted: that a science is best built from the ground up, from a firm foundation of facts that can eventually be summarized as scientific laws.
Book Synopsis Other People's Habits by : Aubrey C. Daniels
Download or read book Other People's Habits written by Aubrey C. Daniels and published by McGraw-Hill Companies. This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applying the powerfully positive, groundbreaking system laid out in "Bringing Out the Best in People", Daniels shows how to use positive reinforcement to live a life filled with harmonious relationships.
Book Synopsis Bringing Out the Best in People by : Aubrey C. Daniels
Download or read book Bringing Out the Best in People written by Aubrey C. Daniels and published by McGraw Hill Professional. This book was released on 2000-01-11 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classic bestseller on performance management is updated to reflect changes in today's working environment. When an employer needs to know how to gain maximum performance from employees, renowned behavioral psychologist--Aubrey Daniels is the man to consult. What has made Daniels the man with the answers? His ability to apply scientifically based behavioral stimuli to the workplace while making it fun at the same time. Now Daniels updates his ground-breaking book with the latest and best motivational methods, perfected at such companies as Xerox, 3M, and Kodak. All-new material shows how to: create effective recognition and rewards systems in line with today's employees want; Stimulate innovations and creativity in new and exciting ways;overcome problems associated with poorly educated workers; motivate young employees from the minute they join the workforce.
Book Synopsis Training the Best Dog Ever by : Larry Kay
Download or read book Training the Best Dog Ever written by Larry Kay and published by Workman Publishing. This book was released on 2012-09-25 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: Training the Best Dog Ever, originally published in hardcover as The Love That Dog Training Program, is a book based on love and kindness. It features a program of positive reinforcement and no-fail techniques that author Dawn Sylvia-Stasiewicz used to train the White House dog, Bo Obama, and each of Senator Ted Kennedy’s dogs, among countless others. Training the Best Dog Ever relies on trust and treats, not choke collars; on bonding, not leash-yanking or reprimanding. The five-week training program takes only 10 to 20 minutes of practice a day and works both for puppies and for adult dogs that need to be trained out of bad habits. Illustrated with step-by-step photographs, the book covers hand-feeding; crate and potty training; and basic cues—sit, stay, come here—as well as more complex goals, such as bite inhibition and water safety. It shows how to avoid or correct typical behavior problems, including jumping, barking, and leash-pulling. Plus: how to make your dog comfortable in the world—a dog that knows how to behave in a vet’s office, is at ease around strangers, and more. In other words, the best dog ever.
Book Synopsis Contingencies of Reinforcement by : B. F. Skinner
Download or read book Contingencies of Reinforcement written by B. F. Skinner and published by B. F. Skinner Foundation. This book was released on 2014-07-01 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: B. F. Skinner titled this book, Contingencies of Reinforcement, after the heart of his science of behavior. Contingencies relate classes of actions to postcedent events and to the contexts in which those action-postcedent relations occur. The basic processes seem straightforward, but many people do not know or understand the underlying theory. Skinner believed that ‘a theory is essential to the scientific understanding of behavior as a subject matter”. This book presents some of Skinner’s most sophisticated statements about theoretical issues. To his original articles, he added notes to clarify and expand subtle points. The book thus provides an overview of Skinner’s thinking about theory and the philosophy underpinning the science he began.
Book Synopsis The Reinforcement Sensitivity Theory of Personality by : Philip J. Corr
Download or read book The Reinforcement Sensitivity Theory of Personality written by Philip J. Corr and published by Cambridge University Press. This book was released on 2008-04-10 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the major neuropsychological models of personality, developed by world-renowned psychologist Professor Jeffrey Gray, is based upon individual differences in reactions to punishing and rewarding stimuli. This biological theory of personality - now widely known as 'Reinforcement Sensitivity Theory' (RST) - has had a major influence on motivation, emotion and psychopathology research. In 2000, RST was substantially revised by Jeffrey Gray, together with Neil McNaughton, and this revised theory proposed three principal motivation/emotion systems: the 'Fight-Flight-Freeze System' (FFFS), the 'Behavioural Approach System' (BAS) and the 'Behavioural Inhibition System' (BIS). This is the first book to summarise the Reinforcement Sensitivity Theory of personality and bring together leading researchers in the field. It summarizes all of the pre-2000 RST research findings, explains and elaborates the implications of the 2000 theory for personality psychology and lays out the future research agenda for RST.
Book Synopsis Coercion and Its Fallout by : Murray Sidman
Download or read book Coercion and Its Fallout written by Murray Sidman and published by . This book was released on 1989 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Reinforcement Learning with TensorFlow by : Sayon Dutta
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 Green Beans and Ice Cream written by and published by . This book was released on 2013-07-15 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Reinforcement Learning by : Phil Winder Ph.D.
Download or read book Reinforcement Learning written by Phil Winder Ph.D. and published by "O'Reilly Media, Inc.". This book was released on 2020-11-06 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website
Book Synopsis Reinforcement Learning, second edition by : Richard S. Sutton
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.
Book Synopsis Hands-On Reinforcement Learning for Games by : Micheal Lanham
Download or read book Hands-On Reinforcement Learning for Games written by Micheal Lanham and published by Packt Publishing Ltd. This book was released on 2020-01-03 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key FeaturesGet to grips with the different reinforcement and DRL algorithms for game developmentLearn how to implement components such as artificial agents, map and level generation, and audio generationGain insights into cutting-edge RL research and understand how it is similar to artificial general researchBook Description With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications. What you will learnUnderstand how deep learning can be integrated into an RL agentExplore basic to advanced algorithms commonly used in game developmentBuild agents that can learn and solve problems in all types of environmentsTrain a Deep Q-Network (DQN) agent to solve the CartPole balancing problemDevelop game AI agents by understanding the mechanism behind complex AIIntegrate all the concepts learned into new projects or gaming agentsWho this book is for If you’re a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.
Download or read book Learning to Play written by Aske Plaat and published by Springer Nature. This book was released on 2020-11-21 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works. In particular he shows why two-person games of tactics and strategy fascinate scientists, programmers, and game enthusiasts and unite them in a common goal: to create artificial intelligence (AI). After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography. The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It's also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it's valuable for any reader engaged with the philosophical implications of artificial and general intelligence, games represent a modern Turing test of the power and limitations of AI.
