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MAXIMUM LIKELIHOOD INVERSE REINFORCEMENT LEARNING ABSTRACT OF THE DISSERTATION MAXIMUM LIKELIHOOD INVERSE REINFORCEMENT LEARNING
unpublished
Learning desirable behavior from a limited number of demonstrations, also known as inverse reinforcement learning, is a challenging task in machine learning. I apply maximum likelihood estimation to the problem of inverse reinforcement learning, and show that it quickly and successfully identifies the unknown reward function from traces of optimal or near-optimal behavior , under the assumption that the reward function is a linear function of a known set of features. I extend this approach to
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