Motivated Reinforcement Learning
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| Author | : Gianluca Baldassarre |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 453 |
| Release | : 2013-03-29 |
| Genre | : Computers |
| ISBN | : 3642323758 |
It has become clear to researchers in robotics and adaptive behaviour that current approaches are yielding systems with limited autonomy and capacity for self-improvement. To learn autonomously and in a cumulative fashion is one of the hallmarks of intelligence, and we know that higher mammals engage in exploratory activities that are not directed to pursue goals of immediate relevance for survival and reproduction but are instead driven by intrinsic motivations such as curiosity, interest in novel stimuli or surprising events, and interest in learning new behaviours. The adaptive value of such intrinsically motivated activities lies in the fact that they allow the cumulative acquisition of knowledge and skills that can be used later to accomplish fitness-enhancing goals. Intrinsic motivations continue during adulthood, and in humans they underlie lifelong learning, artistic creativity, and scientific discovery, while they are also the basis for processes that strongly affect human well-being, such as the sense of competence, self-determination, and self-esteem. This book has two aims: to present the state of the art in research on intrinsically motivated learning, and to identify the related scientific and technological open challenges and most promising research directions. The book introduces the concept of intrinsic motivation in artificial systems, reviews the relevant literature, offers insights from the neural and behavioural sciences, and presents novel tools for research. The book is organized into six parts: the chapters in Part I give general overviews on the concept of intrinsic motivations, their function, and possible mechanisms for implementing them; Parts II, III, and IV focus on three classes of intrinsic motivation mechanisms, those based on predictors, on novelty, and on competence; Part V discusses mechanisms that are complementary to intrinsic motivations; and Part VI introduces tools and experimental frameworks for investigating intrinsic motivations. The contributing authors are among the pioneers carrying out fundamental work on this topic, drawn from related disciplines such as artificial intelligence, robotics, artificial life, evolution, machine learning, developmental psychology, cognitive science, and neuroscience. The book will be of value to graduate students and academic researchers in these domains, and to engineers engaged with the design of autonomous, adaptive robots. The contributing authors are among the pioneers carrying out fundamental work on this topic, drawn from related disciplines such as artificial intelligence, robotics, artificial life, evolution, machine learning, developmental psychology, cognitive science, and neuroscience. The book will be of value to graduate students and academic researchers in these domains, and to engineers engaged with the design of autonomous, adaptive robots.
| Author | : Kathryn E. Merrick |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 206 |
| Release | : 2009-06-12 |
| Genre | : Computers |
| ISBN | : 3540891870 |
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.
| Author | : Deborah Stipek, Ph.D. |
| Publisher | : Holt Paperbacks |
| Total Pages | : 249 |
| Release | : 2014-06-10 |
| Genre | : Family & Relationships |
| ISBN | : 1466873434 |
Motivated Minds--a practical guide to ensuring your child's success in school. What makes students succeed in school? For the past twenty years, the focus has been on building children's self-esteem to help them achieve more in the classroom. But positive reinforcement hasn't necessarily resulted in measureable academic improvement. Through extensive research, combined with ongoing classroom implementation of their ideas, Deborah Stipek, Dean of the School of Education at Stanford, and Kathy Seal have created a program that will encourage motivation and a love of learning in children from toddlerhood through elementary school. Stipek and Seal maintain that parents and teachers can build a solid foundation for learning by helping children to develop the key elements of success: competency, autonomy, curiosity, and critical relationships. The authors offer both practical advice and strategies on understanding different learning styles for Math and reading as well as down-to-earth tips about how to manage difficult issues -- competition, grades, praise, bribes, and rewards -- that inevitably arise for parents and teachers. Most important, Stipek and Seal help parents create an enriching environment for their children at home that will mesh with the school experience and become a positive, effective climate for learning.
| Author | : K. Ann Renninger |
| Publisher | : Cambridge University Press |
| Total Pages | : 1172 |
| Release | : 2019-02-14 |
| Genre | : Psychology |
| ISBN | : 1316832473 |
Written by leading researchers in educational and social psychology, learning science, and neuroscience, this edited volume is suitable for a wide-academic readership. It gives definitions of key terms related to motivation and learning alongside developed explanations of significant findings in the field. It also presents cohesive descriptions concerning how motivation relates to learning, and produces a novel and insightful combination of issues and findings from studies of motivation and/or learning across the authors' collective range of scientific fields. The authors provide a variety of perspectives on motivational constructs and their measurement, which can be used by multiple and distinct scientific communities, both basic and applied.
| Author | : Vieri Giuliano Santucci |
| Publisher | : Frontiers Media SA |
| Total Pages | : 286 |
| Release | : 2020-02-19 |
| Genre | : |
| ISBN | : 288963485X |
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 347 |
| Release | : 2018-09-27 |
| Genre | : Education |
| ISBN | : 0309459672 |
There are many reasons to be curious about the way people learn, and the past several decades have seen an explosion of research that has important implications for individual learning, schooling, workforce training, and policy. In 2000, How People Learn: Brain, Mind, Experience, and School: Expanded Edition was published and its influence has been wide and deep. The report summarized insights on the nature of learning in school-aged children; described principles for the design of effective learning environments; and provided examples of how that could be implemented in the classroom. Since then, researchers have continued to investigate the nature of learning and have generated new findings related to the neurological processes involved in learning, individual and cultural variability related to learning, and educational technologies. In addition to expanding scientific understanding of the mechanisms of learning and how the brain adapts throughout the lifespan, there have been important discoveries about influences on learning, particularly sociocultural factors and the structure of learning environments. How People Learn II: Learners, Contexts, and Cultures provides a much-needed update incorporating insights gained from this research over the past decade. The book expands on the foundation laid out in the 2000 report and takes an in-depth look at the constellation of influences that affect individual learning. How People Learn II will become an indispensable resource to understand learning throughout the lifespan for educators of students and adults.
| Author | : Edward L. Deci |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 325 |
| Release | : 2012-12-06 |
| Genre | : Science |
| ISBN | : 1461344468 |
As I begin to write this Preface, I feel a rush of excitement. I have now finished the book; my gestalt is coming into completion. Throughout the months that I have been writing this, I have, indeed, been intrinsically motivated. Now that it is finished I feel quite competent and self-determining (see Chapter 2). Whether or not those who read the book will perceive me that way is also a concern of mine (an extrinsic one), but it is a wholly separate issue from the intrinsic rewards I have been experiencing. This book presents a theoretical perspective. It reviews an enormous amount of research which establishes unequivocally that intrinsic motivation exists. Also considered herein are various approaches to the conceptualizing of intrinsic motivation. The book concentrates on the approach which has developed out of the work of Robert White (1959), namely, that intrinsically motivated behaviors are ones which a person engages in so that he may feel competent and self-determining in relation to his environment. The book then considers the development of intrinsic motiva tion, how behaviors are motivated intrinsically, how they relate to and how intrinsic motivation is extrinsically motivated behaviors, affected by extrinsic rewards and controls. It also considers how changes in intrinsic motivation relate to changes in attitudes, how people attribute motivation to each other, how the attribution process is motivated, and how the process of perceiving motivation (and other internal states) in oneself relates to perceiving them in others.
| Author | : Katsutoshi Yada |
| Publisher | : Springer |
| Total Pages | : 250 |
| Release | : 2021-07-23 |
| Genre | : Technology & Engineering |
| ISBN | : 9783030731120 |
This book contains expanded versions of research papers presented at the international sessions of Annual Conference of the Japanese Society for Artificial Intelligence (JSAI), which was held online in June 2020. The JSAI annual conferences are considered key events for our organization, and the international sessions held at these conferences play a key role for the society in its efforts to share Japan’s research on artificial intelligence with other countries. In recent years, AI research has proved of great interest to business people. The event draws both more and more presenters and attendees every year, including people of diverse backgrounds such as law and the social sciences, in additional to artificial intelligence. We are extremely pleased to publish this collection of papers as the research results of our international sessions.
| Author | : Robert Schramm |
| Publisher | : Lulu.com |
| Total Pages | : 418 |
| Release | : 2011-05-04 |
| Genre | : Education |
| ISBN | : 1447748360 |
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.
| Author | : Todd Hester |
| Publisher | : Springer |
| Total Pages | : 170 |
| Release | : 2013-06-22 |
| Genre | : Technology & Engineering |
| ISBN | : 3319011685 |
This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuous state features; 3) it must handle sensor and/or actuator delays; and 4) it should continually select actions in real time. This book focuses on addressing all four of these challenges. In particular, this book is focused on time-constrained domains where the first challenge is critically important. In these domains, the agent’s lifetime is not long enough for it to explore the domains thoroughly, and it must learn in very few samples.
