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Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 0262352702
Category : Computers
Languages : en
Pages : 549
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Book Description
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.
Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 0262352702
Category : Computers
Languages : en
Pages : 549
Get Book Here
Book Description
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.
Author: Heidi Grant
Publisher: Harvard Business Press
ISBN: 1633692361
Category : Business & Economics
Languages : en
Pages : 162
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Book Description
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.
Author: B. F. Skinner
Publisher: B. F. Skinner Foundation
ISBN: 0989983951
Category : Psychology
Languages : en
Pages : 794
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Book Description
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.
Author: Robert Glaser
Publisher:
ISBN:
Category : Reinforcement (Psychology)
Languages : en
Pages : 0
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Book Description
Author: Kay Laurence
Publisher: Sunshine Books (MA)
ISBN: 9781890948405
Category : Pets
Languages : en
Pages : 105
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Book Description
Author: Phil Winder Ph.D.
Publisher: "O'Reilly Media, Inc."
ISBN: 1492072346
Category : Computers
Languages : en
Pages : 517
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Book Description
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
Author: International Correspondence Schools
Publisher:
ISBN:
Category : Concrete
Languages : en
Pages : 736
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Book Description
Author: T. Stuart Watson
Publisher: Springer Science & Business Media
ISBN: 9780306484803
Category : Psychology
Languages : en
Pages : 408
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Book Description
- One volume-reference work with approximately 250 entries, organized alphabetically for ease of use and of locating subject matter. Each entry will contain 5-8 references as well as a bibliography of references and suggested readings - An authoritative reference text on school psychology that would appeal to, and be understood by, a broad audience. - Will assist individuals in acquiring a general understanding of some of the theories, practices, and language associated with the field of school psychology
Author: Alexander Zai
Publisher: Manning
ISBN: 1617295434
Category : Computers
Languages : en
Pages : 381
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Book Description
Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap
Author: Jun Otani
Publisher: CRC Press
ISBN: 1000942503
Category : Technology & Engineering
Languages : en
Pages : 917
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Book Description
Earth reinforcement techniques are used worldwide, providing dependable solutions to a wide range of geotechnical engineering problems.Well-established earth reinforcement technologies are regularly augmented by new materials, innovative construction techniques and advances in design and analysis. Furthermore, reinforced earth structures are increasingly seen as expedient and economical techniques in disaster situations, such as earthquakes, flooding or tsunamis. NEW HORIZONS in EARTH REINFORCEMENT contains contributions from the 5th International Symposium on Earth Reinforcement, Kyushu, Japan, 14-16 November 2007, and presents the very latest earth reinforcement techniques and design procedures. The volume showcases advances in materials and emerging applications, with special emphasis on disaster mitigation and geoenvironmental issues. The book will be invaluable to academics and professionals in geotechnical engineering.
