Discounted MDP's: distribution functions and exponential utility maximization
SIAM Journal on Control and Optimization
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
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Negative dynamic programming for risk-sensitive control is studied. Under some compactness and semicontinuity assumptions the following results are proved: the convergence of the value iteration algorithm to the optimal expected total reward, the Borel measurability or upper semicontinuity of the optimal value functions, and the existence of an optimal stationary policy.