Titlebook: Advances in Robot Learning; 8th European Workhop Jeremy Wyatt,John Demiris Conference proceedings 2000 Springer-Verlag Berlin Heidelberg 20

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Egon Krause,Yurii I. Shokin,Nina Shokinaul properties such as the ability to generalize or to be noise-tolerant. Since the process to evolve such controllers in the real-world is very time-consuming, one usually uses simulators to speed up the evolutionary process. By doing so a new problem arises: The controllers evolved in the simulator

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水獺

Alexey Androsov,J?rn Behrens,Sergey Danilovic programming (ILP). The method repeatedly applies induction from examples collected by using previously induced results. This method is effective in a situation where we can only give an inaccurate teacher. We examined this method by applying it to robot learning, which resulted in increasing the

MELD

Kwangcheol Shin,Ajith Abraham,Sang Yong Hanamically updated as information comes to hand during the learning process. Excessive variance of these estimators can be problematic, resulting in uneven or unstable learning, or even making effective learning impossible. Estimator variance is usually managed only indirectly, by selecting global lea

組成

RACE

Dendritic-Cells

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Ankylo-

Learning a Navigation Task in Changing Environments by Multi-task Reinforcement Learning,re of the robot. Finally, we investigate the capabilities of the learning algorithm with respect to the transfer of information between related reinforcement learning tasks, like navigation tasks in different environments. It is hoped that this method will lead to a speed-up in reinforcement learnin

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