Effects of system parameters on the optimal policy structure in a class of queueing control problems
Queueing Systems: Theory and Applications
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Media Revenue Management with Audience Uncertainty: Balancing Upfront and Spot Market Sales
Manufacturing & Service Operations Management
A dynamical games approach to transmission-rate adaptation in multimedia WLAN
IEEE Transactions on Signal Processing
Smarter sampling in model-based Bayesian reinforcement learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Parametric concavity in stochastic dynamic programs
Computers and Industrial Engineering
A decision support model for tax revenue collection in Greece
Decision Support Systems
Operations Research Letters
Monotonicity in multidimensional Markov decision processes for the batch dispatch problem
Operations Research Letters
Technology Adoption with Uncertain Future Costs and Quality
Operations Research
| Hi-index | 0.00 |
In Markov models of sequential decision processes, one is often interested in showing that the value function is monotonic, convex, and/or supermodular in the state variables. These kinds of results can be used to develop a qualitative understanding of the model and characterize how the results will change with changes in model parameters. In this paper we present several fundamental results for establishing these kinds of properties. The results are, in essence, "metatheorems" showing that the value functions satisfy propertyP if the reward functions satisfy propertyP and the transition probabilities satisfy a stochastic version of this property. We focus our attention on closed convex cone properties, a large class of properties that includes monotonicity, convexity, and supermodularity, as well as combinations of these and many other properties of interest.