@misc{cogprints2511,
          volume = {94},
          editor = {Christopher G. Prince and Yiannis Demiris and Yuval Marom and Hideki Kozima and Christian Balkenius},
           title = {Novelty and Reinforcement Learning in the Value System of Developmental Robots},
          author = {Xiao Huang and John Weng},
       publisher = {Lund University Cognitive Studies},
            year = {2002},
           pages = {47--55},
        keywords = {developmental robot, value system, sensory, habituation effect, reinforcement learning, IHDR, SAIL},
             url = {http://cogprints.org/2511/},
        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.}
}

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