creators_name: Huang, Xiao creators_name: Weng, John editors_name: Prince, Christopher G. editors_name: Demiris, Yiannis editors_name: Marom, Yuval editors_name: Kozima, Hideki editors_name: Balkenius, Christian type: confpaper datestamp: 2003-10-04 lastmod: 2011-03-11 08:55:03 metadata_visibility: show title: Novelty and Reinforcement Learning in the Value System of Developmental Robots ispublished: pub subjects: comp-sci-stat-model subjects: comp-sci-mach-learn subjects: comp-sci-art-intel subjects: comp-sci-robot full_text_status: public keywords: developmental robot, value system, sensory, habituation effect, reinforcement learning, IHDR, SAIL abstract: The value system of a developmental robot signals the occurrence of salient sensory inputs, modulates the mapping from sensory inputs to action outputs, and evaluates candidate actions. In the work reported here, a low level value system is modeled and implemented. It simulates the non-associative animal learning mechanism known as habituation effect. Reinforcement learning is also integrated with novelty. Experimental results show that the proposed value system works as designed in a study of robot viewing angle selection. date: 2002 date_type: published volume: 94 publisher: Lund University Cognitive Studies pagerange: 47-55 refereed: TRUE citation: Huang, Xiao and Weng, John (2002) Novelty and Reinforcement Learning in the Value System of Developmental Robots. [Conference Paper] document_url: http://cogprints.org/2511/1/Huang.pdf