Peshkin leonid (8 Ergebnisse)

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Zustand: New. Today we live in the world which is very much aman-made or artificial. In such a world there aremany systems and environments, both real andvirtual, which can be very well described by formalmodels. This creates an opportunity for de.

Taschenbuch. Zustand: Neu. Reinforcement Learning from Scarce Experience via Policy Search | Learning to Act by Reasoning about Trial and Error in Uncertain Environment | Leonid Peshkin | Taschenbuch | Kartoniert / Broschiert | Englisch | 2013 | VDM Verlag Dr. Müller | EAN 9783639088038 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.…

Paperback. Zustand: New.

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Today we live in the world which is very much aman-made or artificial. In such a world there aremany systems and environments, both real andvirtual, which can be very well described by formalmodels. This creates an opportunity for developing a'synthetic intelligence' - artificial systemswhich cohabit these environments with human beings and carry out some useful function. In this book we address some aspects of thisdevelopment in the framework of reinforcementlearning, learning how to map sensations to actions,by trial and error from feedback. In some challengingcases, actions may affect not only the immediatereward, but also the next sensationand all subsequent rewards. The general task ofreinforcement learning stated in a traditional way isunreasonably ambitious for these two characteristics:search by trial-and-error and delayed reward. Weinvestigate general ways of breaking the task ofdesigning a controller down to more feasiblesub-tasks which are solved independently. We proposeto consider both taking advantage of past experienceby reusing parts of other systems, and facilitatingthe learning phase by employing a bias in initialconfiguration.…