Giorgio Manganini

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Classical approximate dynamic programming techniques based on state-space gridding become computationally impracticable for high-dimensional problems. Policy search techniques cope with this curse of dimensionality issue by searching for the optimal control policy in a restricted parameterized policy space. We here focus on the case of discrete action space(More)
This paper investigates the use of second-order methods to solve Markov Decision Processes (MDPs). Despite the popularity of second-order methods in optimization literature, so far little attention has been paid to the extension of such techniques to face sequential decision problems. Here we provide a model-free Reinforcement Learning method that estimates(More)
This paper deals with supervised learning for classification. A new general purpose classifier is proposed that builds upon the Guaranteed Error Machine (GEM). Standard GEM can be tuned to guarantee a desired (small) misclassification probability and this is achieved by letting the classifier return an unknown label. In the proposed classifier, the size of(More)
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