Dip narayan ray (6 resultados)
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Librería: moluna, Greven, Alemaniamoluna
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EUR 63,42
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
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Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books
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EUR 176,04
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Paperback. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 79,00
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Mobile robots are being popular due to their extensive application in different hazardous/ unapproachable areas, such as outer space, underwater explorations, underground coal mines monitoring, inspection in chemical/toxic/ nuclear…factories etc. The conventional/ classical robotics may not serve the purpose well if these environments are totally unknown/unpredictable (even the programmer cannot imagine it). In such cases robot learning may be the best option. Learning from the past experiences, is one such way for real time application of robots for completely unknown environments. Reinforcement learning is one of the best learning methods for robots using a constant system-environment interaction. Both single and multi-agent concepts are available for implementation of learning. The current research work describes a multi-agent based reinforcement learning using the concept of behaviour-based robotics for autonomous exploration of mobile robots. 272 pp. Englisch.
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Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 65,80
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Autonomous Navigation of Mobile Robots | A Fusion of Behaviour-based Robotics and Reinforcement Learning | Dip Narayan Ray | Taschenbuch | 272 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659198083 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19,… 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 79,00
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Mobile robots are being popular due to their extensive application in different hazardous/ unapproachable areas, such as outer space, underwater explorations, underground coal mines monitoring, inspection in chemical/toxic/ nuclear fact…ories etc. The conventional/ classical robotics may not serve the purpose well if these environments are totally unknown/unpredictable (even the programmer cannot imagine it). In such cases robot learning may be the best option. Learning from the past experiences, is one such way for real time application of robots for completely unknown environments. Reinforcement learning is one of the best learning methods for robots using a constant system-environment interaction. Both single and multi-agent concepts are available for implementation of learning. The current research work describes a multi-agent based reinforcement learning using the concept of behaviour-based robotics for autonomous exploration of mobile robots.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 272 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 79,95
Envío por EUR 62,12Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Mobile robots are being popular due to their extensive application in different hazardous/ unapproachable areas, such as outer space, underwater explorations, underground coal mines monitoring, inspection in chemical/toxic/ nuclear facto…ries etc. The conventional/ classical robotics may not serve the purpose well if these environments are totally unknown/unpredictable (even the programmer cannot imagine it). In such cases robot learning may be the best option. Learning from the past experiences, is one such way for real time application of robots for completely unknown environments. Reinforcement learning is one of the best learning methods for robots using a constant system-environment interaction. Both single and multi-agent concepts are available for implementation of learning. The current research work describes a multi-agent based reinforcement learning using the concept of behaviour-based robotics for autonomous exploration of mobile robots.
