In this paper, we propose a new concept, thinning-out, for reducing the number of trials in skill discovery. Thinning-out means to skip over such trials that are unlikely to improve discovering results, in the same way as "pruning" in a search tree. We show that our thinning-out technique significantly reduces the number of trials. In addition, we apply thinning-out to the discovery of good physical motions by legged robots in a simulation environment. By using thinning-out, our virtual robots can discover sophisticated motions that is much different from the initial motion in a reasonable amount of trials. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Kobayashi, H., Hatano, K., Ishino, A., & Shinohara, A. (2007). Reducing trials by thinning-out in skill discovery. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4755 LNAI, pp. 127–138). Springer Verlag. https://doi.org/10.1007/978-3-540-75488-6_13
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