Lab Meeting August 31, 2009 (Jim): Maximum Entropy Inverse Reinforcement Learning

I will try to present this paper instead of the previous one.

Title: Maximum Entropy Inverse Reinforcement Learning
B. D. Ziebart, A. Maas, J. A. Bagnell, and A. K. Dey.
AAAI Conference on Artificial Intelligence (AAAI 2008)

Abstract:
In this work, we develop a probabilistic approach based on the principle of maximum entropy. Our approach providesa well-defined, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods.

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