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Advances in Robot Learning: 8th European Workshop on by Ulrich Nehmzow (auth.), Jeremy Wyatt, John Demiris (eds.)

By Ulrich Nehmzow (auth.), Jeremy Wyatt, John Demiris (eds.)

This publication constitutes the completely refereed post-workshop lawsuits of the eighth eu Workshop on studying Robots, EWLR'99, held in Lausanne, Switzerland in September 1999.
The seven revised complete workshop papers offered have been conscientiously reviewed and chosen for inclusion within the ebook. additionally integrated are invited complete papers. one of the subject matters addressed are map construction for robotic navigation, multi-task reinforcement studying, neural community ways, example-based studying, positioned brokers, making plans maps for cellular robots, course discovering, independent robots, and biologically encouraged approaches.

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These findings stem from investigations made with the lobster’s stomatogastric nervous system in which certain active neurons diffuse 46 P. Eggenberger et al. neuromodulators which then rearrange the networks. Note that the effect of a neuromodulator depends not only on theses substances, but also on the specific receptors, which are differently expressed in different cells. Imagine two cells A and B which expresses two different receptors C and D. A neuromodulator N will then only influence the cell(or synapse) which expresses the receptor A and not the cell with receptor B.

Schaal and C. G. Atkeson. Robot juggling: An implementation of memory-based learning. Control Systems Magazine, 14, 1994. 28. R. S. Sutton. Learning to predict by the methods of temporal differences. Machine Learning, 3:9–44, 1988. 29. R. S. Sutton and A. G. Barto. Reinforcement learning: An Introduction. MIT Press, 1998. 30. S. Thrun. Explanation-based neural network learning: A lifelong learning approach. Kluwer Academic, Norwell, MA, USA, 1996. 31. S. Thrun and L. Pratt, editors. Learning to Learn.

C Springer-Verlag Berlin Heidelberg 2000 24 A. Großmann and R. Poli the learning algorithm is to find a policy, mapping environment states to actions, that maximises the reward over time. Reinforcement learning methods try to find an optimal policy by computing a cumulative performance measure from immediate rewards using statistical methods and dynamic programming. There are a number of reinforcement learning techniques that work effectively on a variety of small problems [1, 24, 32]. But only few of them scale up well to larger problems.

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