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- W1570787710 abstract "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 the optimal value of the parameter of the (cost-to-go) function approximator achievable by temporal-difference learning is determined as a function of stepsize.KeywordsTemporal-difference learningreinforcement learningdynamic programmingcost-to-go function approximationMarkov chains" @default.
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- W1570787710 date "1999-01-01" @default.
- W1570787710 modified "2023-10-16" @default.
- W1570787710 title "On the Asymptotic Behavior of a Constant Stepsize Temporal-Difference Learning Algorithm" @default.
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- W1570787710 doi "https://doi.org/10.1007/3-540-49097-3_11" @default.
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