Download or read book Motivated Reinforcement Learning written by Kathryn E. Merrick and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Intrinsically Motivated Learning in Natural and Artificial Systems written by Gianluca Baldassarre and published by Springer Science & Business Media. This book was released on 2013-03-29 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Intrinsically Motivated Reinforcement Learning written by and published by . This book was released on 2005 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: Psychologists call behavior intrinsically motivated when it is engaged in for its own sake rather than as a step toward solving a specific problem of clear practical value. But what we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated reinforcement learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy.
Download or read book Intrinsically Motivated Open Ended Learning in Autonomous Robots written by Vieri Giuliano Santucci and published by Frontiers Media SA. This book was released on 2020-02-19 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Intrinsically Motivated Reinforcement Learning A Promising Framework for Developmental Robot Learning written by and published by . This book was released on 2005 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the primary challenges of developmental robotics is the question of how to learn and represent increasingly complex behavior in a self-motivated, open-ended way Barto, Singh, and Chentanez (Barto, Singh, & Chentanez 2004; Singh, Barto, & Chentanez 2004) have recently presented an algorithm for intrinsically motivated reinforcement learning that strives to achieve broad competence in an environment in a task-nonspecific manner by incorporating internal reward to build a hierarchical collection of skills. This paper suggests that with its emphasis on task-general, self-motivated, and hierarchical learning, intrinsically motivated reinforcement learning is an obvious choice for organizing behavior in developmental robotics. We present additional preliminary results from a gridworld abstraction of a robot environment and advocate a layered learning architecture for applying the algorithm on a physically embodied system.
Download or read book Intrinsic motivations and open ended development in animals humans and robots written by Gianluca Baldassarre and published by Frontiers E-books. This book was released on 2015-02-10 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this Research Topic for Frontiers in Psychology under the section of Cognitive Science and Frontiers in Neurorobotics is to present state-of-the-art research, whether theoretical, empirical, or computational investigations, on open-ended development driven by intrinsic motivations. The topic will address questions such as: How do motivations drive learning? How are complex skills built up from a foundation of simpler competencies? What are the neural and computational bases for intrinsically motivated learning? What is the contribution of intrinsic motivations to wider cognition? Autonomous development and lifelong open-ended learning are hallmarks of intelligence. Higher mammals, and especially humans, engage in activities that do not appear to directly serve the goals of survival, reproduction, or material advantage. Rather, a large part of their activity is intrinsically motivated - behavior driven by curiosity, play, interest in novel stimuli and surprising events, autonomous goal-setting, and the pleasure of acquiring new competencies. This allows the cumulative acquisition of knowledge and skills that can later be used to accomplish fitness-enhancing goals. Intrinsic motivations continue during adulthood, and in humans artistic creativity, scientific discovery, and subjective well-being owe much to them. The study of intrinsically motivated behavior has a long history in psychological and ethological research, which is now being reinvigorated by perspectives from neuroscience, artificial intelligence and computer science. For example, recent neuroscientific research is discovering how neuromodulators like dopamine and noradrenaline relate not only to extrinsic rewards but also to novel and surprising events, how brain areas such as the superior colliculus and the hippocampus are involved in the perception and processing of events, novel stimuli, and novel associations of stimuli, and how violations of predictions and expectations influence learning and motivation. Computational approaches are characterizing the space of possible reinforcement learning algorithms and their augmentation by intrinsic reinforcements of different kinds. Research in robotics and machine learning is yielding systems with increasing autonomy and capacity for self-improvement: artificial systems with motivations that are similar to those of real organisms and support prolonged autonomous learning. Computational research on intrinsic motivation is being complemented by, and closely interacting with, research that aims to build hierarchical architectures capable of acquiring, storing, and exploiting the knowledge and skills acquired through intrinsically motivated learning. Now is an important moment in the study of intrinsically motivated open-ended development, requiring contributions and integration across a large number of fields within the cognitive sciences. This Research Topic aims to contribute to this effort by welcoming papers carried out with ethological, psychological, neuroscientific and computational approaches, as well as research that cuts across disciplines and approaches.
Download or read book Motivated Minds written by Deborah Stipek, Ph.D. and published by Holt Paperbacks. This book was released on 2014-06-10 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book The Cambridge Handbook of Motivation and Learning written by K. Ann Renninger and published by Cambridge University Press. This book was released on 2019-02-14 with total page 1172 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book How People Learn II written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2018-09-27 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Information driven Intrinsic Motivation in Reinforcement Learning written by Keyan Zahedi and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the main challenges in the field of embodied artificial intelligence is the open-ended autonomous learning of complex behaviours. Our approach is to use task-independent, information-driven intrinsic motivation(s) to support task-dependent learning. The work presented here is a preliminary step in which we investigate the predictive information (the mutual information of the past and future of the sensor stream) as an intrinsic drive, ideally supporting any kind of task acquisition. Previous experiments have shown that the predictive information (PI) is a good candidate to support autonomous, open-ended learning of complex behaviours, because a maximisation of the PI corresponds to an exploration of morphology- and environment-dependent behavioural regularities. The idea is that these regularities can then be exploited in order to solve any given task. Three different experiments are presented and their results lead to the conclusion that the linear combination of the one-step PI with an external reward function is not generally recommended in an episodic policy gradient setting. Only for hard tasks a great speed-up can be achieved at the cost of an asymptotic performance lost.
Download or read book Computational Models of Motivation for Game Playing Agents written by Kathryn E. Merrick and published by Springer. This book was released on 2016-09-22 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this book is on three influential cognitive motives: achievement, affiliation, and power motivation. Incentive-based theories of achievement, affiliation and power motivation are the basis for competence-seeking behaviour, relationship-building, leadership, and resource-controlling behaviour in humans. In this book we show how these motives can be modelled and embedded in artificial agents to achieve behavioural diversity. Theoretical issues are addressed for representing and embedding computational models of motivation in rule-based agents, learning agents, crowds and evolution of motivated agents. Practical issues are addressed for defining games, mini-games or in-game scenarios for virtual worlds in which computer-controlled, motivated agents can participate alongside human players. The book is structured into four parts: game playing in virtual worlds by humans and agents; comparing human and artificial motives; game scenarios for motivated agents; and evolution and the future of motivated game-playing agents. It will provide game programmers, and those with an interest in artificial intelligence, with the knowledge required to develop diverse, believable game-playing agents for virtual worlds.
Download or read book Advances in Artificial Intelligence written by Katsutoshi Yada and published by Springer. This book was released on 2021-07-23 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Motivation and Reinforcement written by Robert Schramm and published by Lulu.com. This book was released on 2011-05-04 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Psychology of Learning and Motivation written by Brian H. Ross and published by Elsevier. This book was released on 2002-06-18 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Psychology of Learning and Motivation publishes empirical and theoretical contributions in cognitive and experimental psychology, ranging from classical and instrumental conditioning to complex learning and problem solving. Each chapter provides a thoughtful integration of a body of work. Volume 41 includes in its coverage chapters on multimedia learning, brain imaging, and memory, among others.
Download or read book Rewards and Intrinsic Motivation written by Judy Cameron and published by Bloomsbury Publishing USA. This book was released on 2002-05-30 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past 30 years, many social psychologists have been critical of the practice of using incentive systems in business, education, and other applied settings. The concern is that money, high grades, prizes, and even praise may be effective in getting people to perform an activity but performance and interest are maintained only so long as the reward keeps coming. Once the reward is withdrawn, the concern is that individuals will enjoy the activity less, perform at a lower level, and spend less time on the task. The claim is that rewards destroy people's intrinsic motivation. Widely accepted, this view has been enormously influential and has led many employers, teachers, and other practitioners to question the use of rewards and incentive systems in applied settings. Contrary to this view, the research by Cameron and Pierce indicates that rewards can be used effectively to enhance interest and performance. The book centers around the debate on rewards and intrinsic motivation. Based on historical, narrative, and meta-analytic reviews, Cameron and Pierce show that, contrary to many claims, rewards do not have pervasive negative effects. Instead, the authors show that careful arrangement of rewards enhances motivation, performance, and interest. The overall goal of the book is to draw together over 30 years of research on rewards, motivation, and performance and to provide practitioners with techniques for designing effective incentive systems.
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.
Download or read book Intrinsically Motivated Exploration in Hierarchical Reinforcement Learning written by Christopher M. Vigorito and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The acquisition of hierarchies of reusable skills is one of the distinguishing characteristics of human intelligence, and the learning of such hierarchies is an important open problem in computational reinforcement learning (RL). In humans, these skills are learned during a substantial developmental period in which individuals are intrinsically motivated to explore their environment and learn about the effects of their actions. The skills learned during this period of exploration are then reused to great effect later in life to solve many unfamiliar problems very quickly. This thesis presents novel methods for achieving such developmental acquisition of skill hierarchies in artificial agents by rewarding them for using their current skill set to better understand the effects of their actions on unfamiliar parts of their environment, which in turn leads to the formation of new skills and further exploration, in a life-long process of hierarchical exploration and skill learning. In particular, we present algorithms for intrinsically motivated hierarchical exploration of Markov Decision Processes (MDPs) and finite factored MDPs (FMDPs). These methods integrate existing research on temporal abstraction in MDPs, intrinsically motivated RL, hierarchical decomposition of finite FMDPs, Bayesian network structure learning, and information theory to achieve long-term, incremental acquisition of skill hierarchies in these environments. Moreover, we show that the skill hierarchies learned in this fashion afford an agent the ability to solve novel tasks in its environment much more quickly than solving them from scratch. To apply these techniques to environments with representational properties that differ from traditional MDPs and finite FMDPs requires methods for incrementally learning transition models of environments with such representations. Taking a step in this direction, we also present novel methods for incremental model learning in two other types of environments. The first is an algorithm for online, incremental structure learning of transition functions for FMDPs with continuous-valued state and action variables. The second is an algorithm for learning the parameters of a predictive state representation, which serves as a model of partially observable dynamical systems with continuous-valued observations and actions. These techniques serve as a prerequisite to future work applying intrinsically motivated skill learning to these types of environments.
