Discounted Markov decision processes with utility constraints
We consider utility-constrained Markov decision processes. The expected utility of the total discounted reward is maximized subject to multiple expected utility constraints. By introducing a corresponding Lagrange function, a saddle-point theorem of the utility constrained optimization is derived. The existence of a constrained optimal policy is characterized by optimal action sets specified with a parametric utility. (c) 2006 Elsevier Ltd. All rights reserved.
markov decision processes utility constraints saddle-point constrained optimal policy discount criterion lagrange technique