We consider a discrete time Markov decision process, where the objectives are linear combinations of standard discounted rewards, each with a different discount factor. We describe several applications that motivate the recent interest in these criteria. For the special case where a standard discounted cost is to be minimized, subject to a constraint on another standard discounted cost but with a different discount factor, we provide an implementable algorithm for computing an optimal policy.
['Eugene A. Feinberg', 'Adam Shwartz']
Constrained dynamic programming with two discount factors: applications and an algorithm