Apprenticeship learning

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, Assistant Professor in Berkeley's EECS department, and Andrew Ng, Assistant Professor in the 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" [ [http://www.cs.utexas.edu/~ianfasel/OtherPapers/AbbeelNgApprenticeship.pdf Pieter Abbeel, Andrew Ng, “Apprenticeship learning via inverse reinforcement learning.” In 21st International Conference on Machine Learning (ICML). 2005.] ]

AIRP concept is closely related to reinforcement learning 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 reward function is unknown. The algorithms use observing the behavior of an expert for teaching "agent" the best "actions" in certain states of "environment".

References

External links

[http://www.cs.stanford.edu/people/ang//rl-videos/ Videos of robots performing "actions" learned through apprenticeship learning algorithms]


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