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.