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"Both a serious academic text and a delightful story, this book offers a look at a full range of learning theories from behavioral to cognitive. It also covers memory, motivation, connectionism (neural net models), and social learning and concludes with a comprehensive synthesis.Summary:Both a serious academic text and a delightful story, this book offers a look at a full range of learning theories from behavioral to cognitive. It also covers memory, motivation, connectionism (neural net models), and social learning and concludes with a comprehensive synthesis.SynopsisPART I: SCIENCE AND THEORY. 1. Human Learning. PART II: MOSTLY BEHAVIORISTIC THEORIES. 2. Early Behaviorism: Pavlov, Watson, and Guthrie. 3. The Effects of Behavior: Thorndike and Hull. 4. Operant Conditioning: Skinner's Radical Behaviorism. 5. Evolutionary Psychology: Learning, Biology, and the Brain. PART III: THE BEGINNINGS OF MODERN COGNITIVISM. 6. A Transition to Modern Cognitivism: Hebb, Tolman, and the Gestaltists. PART IV: MOSTLY COGNITIVE THEORIES. 7. Three Cognitive Theories: Bruner, Piaget, and Vygotsky. 8. Symbolic Models of the Mind and Neural Networks. 9. Learning and Remembering. 10. Motivation. 11. Social Learning: Bandura's Social Cognitive Theory. PART V: SUMMARY. 12. Summary, Synthesis, and Integration. Epilogue. Glossary. References. Name Index."

"Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade.The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. "