"Known for its currency and clear writing style, THE PRINCIPLES OF LEARNING AND BEHAVIOR provides a comprehensive and systematic introduction to elementary forms of learning that have been the focus of research for much of the twentieth century. The book closely reflects the fie…
"This handbook on operant and classical conditioning reflects the acknowledged similarities in the fundamental principles of the two varieties of learning behavior" "This combined survey of operant and classical conditioning provides professional and academic readers with an up…
"This combined survey of operant and classical conditioning provides professional and academic readers with an up-to-date, inclusive account of a core field of psychology research, with in-depth coverage of the basic theory, its applications, and current topics including behavio…
The rise of cognitive science in the 1960s was widely heralded as a scientific revolution - an interpretation that implied the decline and eventual death of behavioral psychology. Although many forms of behavioral psychology did indeed disappear, there was a striking exception: …
The rise of cognitive science in the 1960s was widely heralded as a scientific revolution - an interpretation that implied the decline and eventual death of behavioral psychology. Although many forms of behavioral psychology did indeed disappear, there was a striking exception: …
"Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems,…
This book presents sequential decision theory from a novel algorithmic information theory perspective. While the former is suited for active agents in known environment, the latter is suited for passive prediction in unknown environment. The book introduces these two different i…
"The book introduces these two well-known but very different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an arbitrary unknown environment. Most if not all Al problems can easily be formula…
The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past deca…
