Abstract. In this study we present a sparse Bayesian framework for value func- tion approximation. The proposed method is based on the on-line construction.
In this study we present a sparse Bayesian framework for value function approximation. The proposed method is based on the on-line construction of a ...
In this study we present a sparse Bayesian framework for value function approximation. The proposed method is based on the on-line construction of a ...
Abstract. In this study we present a sparse Bayesian framework for value func- tion approximation. The proposed method is based on the on-line construction.
A sparse Bayesian framework for value function approximation is presented, based on the on-line construction of a dictionary of states which are collected ...
Feature Selection for Value Function. Approximation Using Bayesian Model Selection. Tobias Jung and Peter Stone. Department of Computer Sciences, University of ...
Jun 6, 2022 · Specifically, our aim is to approximate complex-valued models. A common approach to obtain the coefficients in the surrogate is to minimize the ...
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What is sparse function approximation?
What is value function approximation?
Jul 25, 2013 · ... represent the value function in a tabular form. Value function approximation helps to cope with this problem. Common forms of this approximation ...
If the criterion Qr - Si (defined in Section 3.2) is negative, this maximum occurs at O:i = 00, equivalent to the removal of basis function i from the model.
The notion of setting weights to zero (as distinct from constraining them, say, to small values), is a com- pelling one in terms of controlling model complexity.