Two Jacobi- and Gauss-Seidel-like relative value iteration algorithms for finite-state ergodic risk-sensitive Markov decision processes are proven to converge geometrically under irreducibility and recurrence assumptions.
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Jacobi-like relative value iteration algorithms for ergodic risk-sensitive control of Markov chains
Two Jacobi- and Gauss-Seidel-like relative value iteration algorithms for finite-state ergodic risk-sensitive Markov decision processes are proven to converge geometrically under irreducibility and recurrence assumptions.