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Regularized feature selection in reinforcement learning
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DOI：10.1007/s10994-015-5518-8.png)
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Abstract
En 中文
We introduce feature regularization during feature selection for value function approximation. Feature regularization introduces a prior into the selection process, improving function approximation accuracy and reducing overfitting. We show that the smoothness prior is effective in the incremental feature selection setting and present closed-form smoothness regularizers for the Fourier and RBF bases. We present two methods for feature regularization which extend the temporal difference orthogonal matching pursuit (OMP-TD) algorithm and demonstrate the effectiveness of the smoothness prior; smooth Tikhonov OMP-TD and smoothness scaled OMP-TD. We compare these methods against OMP-TD, regularized OMP-TD and least squares TD with random projections, across six benchmark domains using two different types of basis functions.
Keywords:
Feature selection
Reinforcement learning
Function approximation
Regularization
Linear function approximation
OMP-TD
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