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An Algorithmic Perspective on Imitation Learning (Paperback)
Loot Price: R2,292
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An Algorithmic Perspective on Imitation Learning (Paperback)
Series: Foundations and Trends in Robotics
Expected to ship within 10 - 15 working days
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As robots and other intelligent agents move from simple
environments and problems to more complex, unstructured settings,
manually programming their behavior has become increasingly
challenging and expensive. Often, it is easier for a teacher to
demonstrate a desired behavior rather than attempt to manually
engineer it. This process of learning from demonstrations, and the
study of algorithms to do so, is called imitation learning. An
Algorithmic Perspective on Imitation Learning provides the reader
with an introduction to imitation learning. It covers the
underlying assumptions, approaches, and how they relate; the rich
set of algorithms developed to tackle the problem; and advice on
effective tools and implementation. An Algorithmic Perspective on
Imitation Learning serves two audiences. First, it familiarizes
machine learning experts with the challenges of imitation learning,
particularly those arising in robotics, and the interesting
theoretical and practical distinctions between it and more familiar
frameworks like statistical supervised learning theory and
reinforcement learning. Second, it provides roboticists and experts
in applied artificial intelligence with a broader appreciation for
the frameworks and tools available for imitation learning. It pays
particular attention to the intimate connection between imitation
learning approaches and those of structured prediction.
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