By Ulrich Nehmzow (auth.), Jeremy Wyatt, John Demiris (eds.)
This publication constitutes the completely refereed post-workshop court cases of the eighth ecu Workshop on studying Robots, EWLR'99, held in Lausanne, Switzerland in September 1999.
The seven revised complete workshop papers awarded have been conscientiously reviewed and chosen for inclusion within the e-book. additionally incorporated are invited complete papers. one of the themes addressed are map development for robotic navigation, multi-task reinforcement studying, neural community techniques, 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 resources for Advances in Robot Learning: 8th European Workshop on Learning Robots, EWLR-8 Lausanne, Switzerland, September 18, 1999 Proceedings
Moore. Variable Resolution Dynamic Programming: Eﬃciently learning action maps in multivariate real-valued state spaces. In L. A. Birnbaum and G. C. Collins, editors, Machine Learning: Proceedings of the Eighth International Workshop (ML-91). Morgan Kaufmann, 1991. 22. A. W. Moore. The parti-game algorithm for variable resolution reinforcement learning in multi-dimensional state-spaces. In J. D. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems, volume 6, pages 711–718.
We are currently performing a detailed performance evaluation of model-based (value iteration, prioritised sweeping) and model-free learning paradigms (Q(λ)-learning) in connection with the use of dynamic cell structures as representational tools. 7 Conclusions We have argued that there is a close connection between adaptive state-space quantisation and continual learning. This was the motivation for developing a novel method for learning multiple tasks from reinforcement. This technique, which has been applied to a mobile robot, performs a quantisation of the agentinternal state space using constructive neural networks.
EWLR 1999, LNAI 1812, pp. 44–60, 2000. c Springer-Verlag Berlin Heidelberg 2000 Toward Seamless Transfer from Simulated to Real Worlds 45 In the ER approaches, artiﬁcial neural networks are widely used to construct controllers for autonomous mobile agents, because they can generalize, are non– linear and noise–tolerant [Floreano94,Nolﬁ97,Beer89,Ackley92]. Another advantage of neural network–driven robots is that a neural network is low–level description of a controller. More precisely, it directly maps sensor readings onto motor outputs.
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.)