Advances in Neural Information Processing Systems 19 2007
DOI: 10.7551/mitpress/7503.003.0149 View full text |Buy / Rent full text

Abstract: The Maximum Margin Planning (MMP) (Ratliff et al., 2006) algorithm solves imitation learning problems by learning linear mappings from features to cost functions in a planning domain. The learned policy is the result of minimum-cost planning using these cost functions. These mappings are chosen so that example policies (or trajectories) given by a teacher appear to be lower cost (with lossscaled margin) than any other policy for a given planning domain. We provide a novel approach, MMPBOOST , based on the func… Show more

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