Apprenticeship learning
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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, Assistant 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"[1]
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.
