By Ulrich Nehmzow (auth.), Jeremy Wyatt, John Demiris (eds.)
This ebook constitutes the completely refereed post-workshop court cases of the eighth eu Workshop on studying Robots, EWLR'99, held in Lausanne, Switzerland in September 1999.
The seven revised complete workshop papers provided have been conscientiously reviewed and chosen for inclusion within the ebook. additionally incorporated are invited complete papers. one of the subject matters addressed are map development for robotic navigation, multi-task reinforcement studying, neural community methods, example-based studying, positioned brokers, making plans maps for cellular robots, course discovering, self sufficient robots, and biologically encouraged approaches.
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Additional info for Advances in Robot Learning: 8th European Workshop on Learning Robots, EWLR-8 Lausanne, Switzerland, September 18, 1999 Proceedings
Littman, and A. R. Cassandra. Planning and acting in partially observable stochastic domains. Artiﬁcial Intelligence, 101:99–134, 1998. 10. L. P. Kaelbling, M. L. Littman, and A. W. Moore. Reinforcement learning: A survey. Artiﬁcial Intelligence Research, 4:237–285, 1996. 11. Z. Kalm´ ar, C. Szepesv´ ari, and A. L˝ orincz. Module based reinforcement learning for a real robot. Presented at the Sixth European Workshop on Learning Robots, Brighton (EWLR-6), UK, 1997. 12. T. Kohonen. Self-organising formation of topologically correct feature maps.
Artiﬁcial Intelligence, 55(2-3):311–365, 1992. 17. M. J. Matari´c. Reward functions for accelerated learning. In Proceedings of the Eleventh International Conference on Machine Learning, pages 181–189. Morgan Kaufmann, 1994. 18. A. K. McCallum. Reinforcement learning with selective perception and hidden state. PhD thesis, Department of Computer Science, University of Rochester, Rochester, NY, USA, 1995. Learning a Navigation Task in Changing Environments 43 19. L. A. Meeden. An incremental approach to developing intelligent neural network controllers for robots.
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.
Advances in Robot Learning: 8th European Workshop on Learning Robots, EWLR-8 Lausanne, Switzerland, September 18, 1999 Proceedings by Ulrich Nehmzow (auth.), Jeremy Wyatt, John Demiris (eds.)