Learning to Catch: Applying Nearest Neighbor Algorithms to Dynamic Control Tasks
نویسندگان
چکیده
This paper examines the hypothesis that local weighted variants of k-nearest neighbor algorithms can support dynamic control tasks. We evaluated several k-nearest neighbor (k-NN) algorithms on the simulated learning task of catching a ying ball. Previously, local regression algorithms have been advocated for this class of problems. These algorithms, which are variants of k-NN, base their predictions on a (possibly weighted) regression computed from the k nearest neighbors. While they outperform simpler k-NN algorithms on many tasks, they have trouble on this ball-catching task. We hypothesize that the non-linearities in this task are the cause of this behavior, and that local regression algorithms may need to be modiied to work well under similar conditions.
منابع مشابه
Learning to Catch: Applying Nearest Neighbor Algorithms to Dynamic Control Tasks Extended Abstract
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