Maximum entropy inverse reinforcement learning

Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Prob-lems. This approach reduces learning to the problem of re-covering a utility function that makes the behavior induced by a near-optimal policy closely mimic demonstrated behav-ior. In this work, we develop a probabilistic approach based on the principle of maximum entropy. Our approach provides a well-defined, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods. We develop our technique in the context of modeling real-world navigation and driving behaviors where collected data is inherently noisy and imperfect. Our probabilistic approach enables modeling of route preferences as well as a powerful new approach to inferring destinations and routes based on partial trajectories