Average cost temporal-difference learning
The authors propose a variant of temporal-difference learning that approximates average and differential costs of an irreducible aperiodic Markov chain. Approximations are performed through linear combinations of fixed basis functions whose weights are incrementally updated during a single endless trajectory of the Markov chain. In addition, a proof of convergence and a characterization of the limit of convergence are presented. A bound on the resulting approximation error that exhibits an interesting dependence on ``mixing time'' of the Markov chain is provided.
scientific article; zbMATH DE number 1355384