Abstract
AbstractThis work concerns discrete-time average Markov decision chains on a denumerable state space. Besides standard continuity compactness requirements, the main structural condition on the model is that the cost function has a Lyapunov function ℓ and that a power larger than two of ℓ also admits a Lyapunov function. In this context, the existence of optimal stationary policies in the (strong) sample-path sense is established, and it is shown that the Markov policies obtained from methods commonly used to approximate a solution of the optimality equation are also sample-path average optimal.KeywordsLyapunov FunctionAverage Markov Decision ChainsSample Path OptimizationMarkov PolicyDenumerable State SpaceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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