5 p.m.
TU Berlin, Room MA 043, Straße des 17. Juni 136, 10623 Berlin
Nicole Bäuerle (Karlsruher Institut für Technologie (KIT))

Partially Observable Risk-Sensitive Markov Decision Processes

We consider the problem of minimizing a certainty equivalent of the total or discounted cost over a finite time horizon which is generated by a Partially Observable Markov Decision Process (POMDP). The certainty equivalent of a random variable $X$ is defined by $U^{-1}(EU(X))$ where $U$ is an increasing function. In contrast to a risk-neutral decision maker, this optimization criterion takes the variability of the cost into account. It contains as a special case the classical risk-sensitive optimization criterion with an exponential utility. We show that this optimization problem can be solved by embedding the problem into a completely observable Markov Decision Process with extended state space and give conditions under which an optimal policy exists. The state space has to be extended by the joint conditional distribution of current unobserved state and accumulated cost. In case of an exponential utility, the problem simplifies considerably and we rediscover what in previous literature has been named information state. A simple example, namely a risk-sensitive Bayesian house selling problem is considered to illustrate our results.