Vladislav Tadic

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The asymptotic properties of temporal-difference learning algorithms with linear function approximation are analyzed in this paper. The analysis is carried out in the context of the approximation of a discounted cost-to-go function associated with an uncontrolled Markov chain with an uncountable finite-dimensional state-space. Under mild conditions, the(More)
The mean-square asymptotic behavior of constant stepsize temporal-difference algorithms is analyzed in this paper. The analysis is carried out for the case of a linear (cost-to-go) function approximation and for the case of Markov chains with an uncountable state space. An asymptotic upper bound for the mean-square deviation of the algorithm iterations from(More)
The asymptotic properties of temporal-difference learning algorithms with linear function approximation are analyzed in this paper. The analysis is carried out in the context of the approximation of a discounted cost-to-go function associated to an uncontrolled Markov chain with an uncountable finite-dimensional state-space. Under very mild conditions, the(More)
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