Abstract
A Markov decision problem is called reversible if the stationary controlled Markov chain is reversible under every stationary Markovian strategy. A natural application in which such problems arise is in the control of Metropolis–Hastings type dynamics. We characterize all discrete time reversible Markov decision processes with finite state and action spaces. We show that the policy iteration algorithm for finding an optimal policy can be significantly simplified in Markov decision problems of this type. We also highlight the relation between the finite time evolution of the accrual of reward and the Gaussian free field associated to the controlled Markov chain.
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