Towards a Unified Framework for Learning from Observation
نویسندگان
چکیده
This paper discusses the recent trends in machine learning towards learning from observation (LfO). These reflect a growing interest in having computers learn as humans do — by observing and thereafter imitating the performance of a task or an action. We discuss the basic foundation of this field and the early research in this area. We then proceed to characterize the types of tasks that can be learned from observation and how to evaluate an agent created in this manner. The main contribution of this paper is a joint framework that unifies all previous formalizations of LfO.
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