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An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning

An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning

This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. We show that linear value function approximation is equivalent to a form of linear model approximation. We derive a relationship between the model approximation error and the Bellman error, and show how this relationship can guide feature selection for model improvement and/or value function improvement. We also show how these results give insight into the behavior of existing feature-selection algorithms.

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