Continuous-Time Markov Decision Processes with Controlled Observations

TL;DR

This paper develops a theoretical framework for optimizing observation timing and control actions in continuous-time Markov decision processes with discrete observation instances, applicable to queueing and inventory systems.

Contribution

It introduces a joint optimization approach for observation epochs and control actions in continuous-time MDPs with controlled observations, including explicit solutions and numerical methods.

Findings

Optimal observation is independent of state in gated queueing systems.

More frequent observations are optimal in certain state regions.

Numerical solutions demonstrate the effectiveness of the proposed framework.

Abstract

In this paper, we study a continuous-time discounted jump Markov decision process with both controlled actions and observations. The observation is only available for a discrete set of time instances. At each time of observation, one has to select an optimal timing for the next observation and a control trajectory for the time interval between two observation points. We provide a theoretical framework that the decision maker can utilize to find the optimal observation epochs and the optimal actions jointly. Two cases are investigated. One is gated queueing systems in which we explicitly characterize the optimal action and the optimal observation where the optimal observation is shown to be independent of the state. Another is the inventory control problem with Poisson arrival process in which we obtain numerically the optimal action and observation. The results show that it is optimal to observe more frequently at a region of states where the optimal action adapts constantly.