Maximum Entropy Inverse Reinforcement Learning
Maximum Entropy Inverse Reinforcement Learning Brian D. Ziebart, Andrew Maas, J.Andrew Bagnell, and Anind K. Dey School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 bziebart@cs.cmu.edu, amaas@andrew.cmu.edu, dbagnell@ri.cmu.edu, anind@cs.cmu.edu Abstract Recent research has shown the benefit of framing problems
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