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Author: Anthonie Wurth
Publisher: John Wiley & Sons
ISBN: 1119425557
Category : Business & Economics
Languages : en
Pages : 240
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Book Description
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.
Author: Anthonie Wurth
Publisher: John Wiley & Sons
ISBN: 1119425557
Category : Business & Economics
Languages : en
Pages : 240
Get Book Here
Book Description
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.
Author: John G. Shedd Aquarium
Publisher:
ISBN:
Category : Animal training
Languages : en
Pages : 604
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Book Description
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: 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: Larry Kay
Publisher: Workman Publishing
ISBN: 0761168850
Category : Pets
Languages : en
Pages : 305
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Book Description
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.
Author: Abdelhamid Mellouk
Publisher: BoD – Books on Demand
ISBN: 9533073691
Category : Computers
Languages : en
Pages : 486
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Book Description
Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic.
Author: Tohid Atashbar
Publisher: International Monetary Fund
ISBN:
Category : Business & Economics
Languages : en
Pages : 23
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Book Description
Learning from the past is critical for shaping the future, especially when it comes to economic policymaking. Building upon the current methods in the application of Reinforcement Learning (RL) to the large language models (LLMs), this paper introduces Reinforcement Learning from Experience Feedback (RLXF), a procedure that tunes LLMs based on lessons from past experiences. RLXF integrates historical experiences into LLM training in two key ways - by training reward models on historical data, and by using that knowledge to fine-tune the LLMs. As a case study, we applied RLXF to tune an LLM using the IMF's MONA database to generate historically-grounded policy suggestions. The results demonstrate RLXF's potential to equip generative AI with a nuanced perspective informed by previous experiences. Overall, it seems RLXF could enable more informed applications of LLMs for economic policy, but this approach is not without the potential risks and limitations of relying heavily on historical data, as it may perpetuate biases and outdated assumptions.
Author: Hao Dong
Publisher: Springer Nature
ISBN: 9811540950
Category : Computers
Languages : en
Pages : 526
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Book Description
Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.
Author: Enes Bilgin
Publisher: Packt Publishing Ltd
ISBN: 1838648496
Category : Computers
Languages : en
Pages : 544
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Book Description
Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices Key FeaturesUnderstand how large-scale state-of-the-art RL algorithms and approaches workApply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and moreExplore tips and best practices from experts that will enable you to overcome real-world RL challengesBook Description Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems. What you will learnModel and solve complex sequential decision-making problems using RLDevelop a solid understanding of how state-of-the-art RL methods workUse Python and TensorFlow to code RL algorithms from scratchParallelize and scale up your RL implementations using Ray's RLlib packageGet in-depth knowledge of a wide variety of RL topicsUnderstand the trade-offs between different RL approachesDiscover and address the challenges of implementing RL in the real worldWho this book is for This book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.
Author: Philip Osborne
Publisher: Morgan & Claypool Publishers
ISBN: 1636393454
Category : Computers
Languages : en
Pages : 109
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Book Description
Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals. In some cases, this machine learning approach can save programmers time, outperform existing controllers, reach super-human performance, and continually adapt to changing conditions. It has shown human level performance on a number of tasks (REF) and the methodology for automation in robotics and self-driving cars (REF). This book argues that these successes show reinforcement learning can be adopted successfully in many different situations, including robot control, stock trading, supply chain optimization, and plant control. However, reinforcement learning has traditionally been limited to applications in virtual environments or simulations in which the setup is already provided. Furthermore, experimentation may be completed for an almost limitless number of attempts risk-free. In many real-life tasks, applying reinforcement learning is not as simple as (1) data is not in the correct form for reinforcement learning; (2) data is scarce, and (3) automation has limitations in the real-world. Therefore, this book is written to help academics, domain specialists, and data enthusiast alike to understand the basic principles of applying reinforcement learning to real-world problems. This is achieved by focusing on the process of taking practical examples and modeling standard data into the correct form required to then apply basic agents. To further assist readers gain a deep and grounded understanding of the approaches, the book shows hand-calculated examples in full and then how this can be achieved in a more automated manner with code. For decision makers who are interested in reinforcement learning as a solution but are not proficient, the book includes simple, non-technical examples in the introduction and case studies section. These provide context of what reinforcement learning offer but also the challenges and risks associated with applying it in practice. Specifically, these sections illustrate the differences between reinforcement learning and other machine learning approaches as well as how well-known companies have found success using the approach to their problems.
