Carnegie Mellon University
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Knowledge of Knowledge and Intelligent Experimentation for Learning Control

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journal contribution
posted on 1991-01-01, 00:00 authored by Andrew Moore
It is shown that if a learning system is able to provide some estimate of the reliability of the generalizations it produces, then the rate of learning can be considerably increased. The increase is achieved by a decision-theoretic estimate of the value of trying alternative experimental actions. A further consequence of this kind of learning is that experience becomes concentrated in regions of the control space which are relevant to the task at hand. Such a restriction of experience is essential for continuous multivariate control tasks because the entire state space of such tasks could not possibly be learned in a practical amount of time


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