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Apprenticeship learning
Apprenticeship learning, or apprenticeship via inverse reinforcement learning (AIRP), is a concept in the field of Artificial Intelligence and Machine learning, developed by Pieter Abbeel, Associate Professor in Berkeley's EECS department, and Andrew Ng, Associate Professor in Stanford University's Computer Science Department. AIRP deals with "Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform"〔(Pieter Abbeel, Andrew Ng, “Apprenticeship learning via inverse reinforcement learning.” In 21st International Conference on Machine Learning (ICML). 2004. )〕
AIRP concept is closely related to reinforcement learning (RL) that is a sub-area of Machine learning concerned with how an ''agent'' ought to take ''actions'' in an ''environment'' so as to maximize some notion of long-term ''reward''. AIRP algorithms are used when the reward function is unknown. The algorithms use observations of the behavior of an expert to teach the ''agent'' the optimal ''actions'' in certain states of the ''environment''.
AIRP is a special case of the general area of Learning from Demonstration (LfD), where the goal is to learn a complex task by observing a set of expert traces (demonstrations). AIRP is the intersection of LfD and RL.
==References==
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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