The Adaptive k-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning
published: Aug. 26, 2009, recorded: June 2009, views: 4223
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The purpose of this paper is three-fold. First, we formalize and study a problem of learning probabilistic concepts in the recently proposed KWIK framework. We give details of an algorithm, known as the Adaptive k-Meteorologists Algorithm, analyze its sample complexity upper bound, and give a matching lower bound. Second, this algorithm is used to create a new reinforcement learning algorithm for factoredstate problems that enjoys significant improvement over the previous state-of-the-art algorithm. Finally, we apply the Adaptive k-Meteorologists Algorithm to remove a limiting assumption in an existing reinforcement-learning algorithm. The effectiveness of our approaches are demonstrated empirically in a couple benchmark domains as well as a robotics navigation problem.
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