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In this paper we investigate the computation of optimal policies in constrained discrete stochastic dynamic programming with the average reward as utility function. The state-space and action-sets are assumed to be finite. Constraints which are linear ...

In this paper, we study a Markov decision process with a non-linear discount function and with a Borel state space. We define a recursive discounted utility, which resembles non-additive utility functions considered in a number of models in ...

In this paper we study a Markov decision process with a nonlinear discount function. First, we define a utility on the space of trajectories of the process in the finite and infinite time horizon and then take their expected values. It turns out that ...