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In this paper we present a dynamic neural network that dynamically grows the number of the hidden-layer neurons based on an increase in the entropy of the weights during training. The weights are normalized to probability values prior to the computation of the entropy. The entropy being referred is the non-extensive entropy proposed recently by Susan and Hanmandlu for the representation of structured...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and in this instance, RL techniques require the use of function approximators for learning value functions and policies. Often, local linear models have been preferred over distributed nonlinear models for function approximation in RL. We suggest that one reason for the difficulties encountered when using...
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