In this tutorial, we aim to present to researchers and industry practitioners a broad overview of imitation learning techniques and recent applications. Imitation learning is a powerful and practical alternative to reinforcement learning for learning sequential decision-making policies. Also known as learning from demonstrations or apprenticeship learning, imitation learning has benefited from recent progress in core learning techniques, increased availability & fidelity of demonstration data, as well as the computational advancements brought on by deep learning. We expect this tutorial to be highly relevant for researchers & practitioners who have interests in reinforcement learning, structured prediction, planning and control. The ideal audience member should have familiarity with basic supervised learning concepts. No knowledge of reinforcement learning techniques will be assumed.
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Yisong Yue is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign.
Yisong's research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for interactive machine learning and structured prediction. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, behavior analysis, sports analytics, policy learning in robotics, and adaptive planning & allocation problems.
Hoang M. Le is a PhD Candidate in the Computing and Mathematical Sciences Department at the California Institute of Technology. He received a M.S. in Cognitive Systems and Interactive Media from the Universitat Pompeu Fabra, Barcelona, Spain, and a B.A. in Mathematics from Bucknell University in Lewisburg, PA. He is a recipient of an Amazon AI Fellowship.
Hoang’s research focuses on the theory and applications of sequential decision making, with a strong focus on imitation learning. He has broad familiarity with the latest advances in imitation learning techniques and applications. His own research in imitation learning blends principled new techniques with a diverse range of application domains. In addition to popular RL domains such as maze navigation and Atari games, his prior work on imitation learning has been applied to learning human behavior in team sports and developing automatic camera broadcasting systems.