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We consider the classical finite-state discounted Markovian decision problem, and we introduce a new policy iteration-like algorithm for finding the optimal Q-factors. Instead of policy evaluation by solving a linear system of equations, our algorithm involves (possibly inexact) solution of an optimal stopping problem. This problem can be solved with simple Q-learning iterations, in the case where...
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