Decision roll and horizon roll processes in infinite horizon discounted Markov decision processes

This paper looks at some aspects of infinite horizon Markov decision processes in which information regarding parameter values is restricted to a finite time horizon, and in which decisions are based upon the finite horizon data but are recomputed as we move forward in time and gain knowledge of later parametric values. A general framework is given. Bounds on the loss of optimality arising from two planning horizon decision processes are given, and the choice of decision process and planning horizon is examined.