Reinforcements

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.

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.

This book presents cutting-edge research on the design and development of novel, advanced high-strength, light-weight materials via the incorporation of novel reinforcements, namely, metallic amorphous alloys/bulk metallic glasses (BMG), in light metal matrix composites (LMMCs) based on Al and Mg. The book begins with an introduction to conventional ceramic reinforced light metal matrix composites, along with the major drawbacks which limit their application. Metallic amorphous alloys/Bulk Metallic Glasses (BMG) are new class of metallic materials that are distinctly differently from conventional metals/alloys in terms of their structure and thermal behavior, and exhibit extremely high strength (1 to 2 GPa) and large elastic strain limit (1 to 2%). Given these unique properties, upon their incorporation into Al/Mg-matrices, they provide superior interfacial properties, i.e. high degree of compatibility with the matrix due to their metallic nature when compared to conventional ceramic reinforcements, and thereby significantly enhance the mechanical performance of LMMCs. Amorphous/BMG reinforced LMMCs is an emerging research field and the existing literature is meager. This book discusses the various processing methods that would be suitable for these novel materials. A comparison of mechanical properties and strengthening mechanisms of amorphous/BMG reinforced composites with those of conventional ceramic composites is presented. Future research directions and wider research potential of the novel materials are discussed, and prospective applications are highlighted. For ease of understanding and comparison, appropriate schematics, tables, and figures are provided.

Composite Reinforcements for Optimum Performance, Second Edition, has been brought fully up to date with the latest developments in the field. It reviews the materials, properties and modelling techniques used in composite production and highlights their uses in optimizing performance. Part I covers materials for reinforcements in composites, including chapters on fibers, carbon nanotubes and ceramics as reinforcement materials. In Part II, different types of structures for reinforcements are discussed, with chapters covering woven and braided reinforcements, three-dimensional fibre structures and two methods of modelling the geometry of textile reinforcements: WiseTex and TexGen. Part III focuses on the properties of composite reinforcements, with chapters on topics such as in-plane shear properties, transverse compression, bending and permeability properties. Finally, Part IV covers the characterization and modelling of reinforcements in composites, with chapters focusing on microscopic and mesoscopic approaches, X-ray tomography analysis and modelling reinforcement forming processes. With its distinguished editor and international team of contributors, Composite Reinforcements for Optimum Performance, Second Edition, is an essential reference for designers and engineers working in the composite and composite reinforcement manufacturing industry, as well as all those with an academic research interest in the subject. Discusses the characterization and modeling of reinforcements in composites, focusing on such topics as microscopic and mesoscopic approaches, X-ray tomography analysis, and modeling reinforcement forming processes Provides comprehensive coverage of the types and properties of reinforcement in composites, along with their production and performance optimization Includes sections on NCF (non-crimp fabrics), natural fiber reinforcements, tufting composite reinforcements, sustainability, multiscale modeling, knitted reinforcements, and more

One of Lulu's best sellers of all time, the second edition of the book Educate Toward Recovery is now called Motivation and Reinforcement: Turning the Tables on Autism. This book is the ultimate guide to home based autism intervention. It is a forward-thinking guide that translates the Verbal Behavior Approach to ABA into everyday language. With over 100 new pages of material including new Chapters on Social Skills, Behavior Plans, Token Economies, and Advanced Instructional Control methods, this book is a must have even for those who own the 2006 version. International ABA/VB presenter Robert Schramm, explains how you can keep your child engaged in motivated learning throughout his entire day without forcing participation, blocking escape, or nagging procedures. M&R is the full realization of modern ABA/VB Autism Intervention and a great resource for parents, teachers, and therapists working with a child with autism as well as BCBA's looking for ways to improve their approach.

This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.

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

Natural fiber-reinforced composites have the potential to replace synthetic composites, leading to less expensive, stronger and more environmentally-friendly materials. This book provides a detailed review on how a broad range of biofibers can be used as reinforcements in composites and assesses their overall performance. The book is divided into five major parts according to the origins of the different biofibers. Part I contains chapters on bast fibers, Part II; leaf fibers, Part III; seed fibers, Part IV; grass, reed and cane fibers, and finally Part V covers wood, cellulosic and other fibers including cellulosic nanofibers. Each chapter reviews a specific type of biofiber providing detailed information on the sources of each fiber, their cultivation, how to process and prepare them, and how to integrate them into composite materials. The chapters outline current and potential applications for each fiber and discuss their main strengths and weaknesses. The book is divided into five major parts according to the origins of the different biofibers - bast, leaf, seed; grass, reed and cane fibers, and finally wood, cellulosic and other fibers including cellulosic nanofibers. This book provides a detailed review on how a broad range of biofibers can be used as reinforcements in composites and assesses their overall performance The chapters outline current and potential applications for each fiber and discuss their main strengths and weaknesses