Decision-Dependent Distributionally Robust Markov Decision Process Method in Dynamic Epidemic Control

Distributionally Robust Markov Decision Processes

We consider Markov decision processes where the values of the parameters are uncertain. This uncertainty is described by a sequence of nested sets (that is, each set contains the previous one), each of which corresponds to a probabilistic guarantee for a different confidence level so that a set of admissible probability distributions of the unknown parameters is specified. This formulation mode...

Robust partially observable Markov decision process

We seek to find the robust policy that maximizes the expected cumulative reward for the worst case when a partially observable Markov decision process (POMDP) has uncertain parameters whose values are only known to be in a given region. We prove that the robust value function, which represents the expected cumulative reward that can be obtained with the robust policy, is convex with respect to ...

Distributionally Robust Optimization for Sequential Decision Making

The distributionally robust Markov Decision Process approach has been proposed in the literature, where the goal is to seek a distributionally robust policy that achieves the maximal expected total reward under the most adversarial joint distribution of uncertain parameters. In this paper, we study distributionally robust MDP where ambiguity sets for uncertain parameters are of a format that ca...

Dynamic Decision Model using Partially Observable Markov Decision Process

Robust Markov Decision Processes

Markov decision processes (MDPs) are powerful tools for decision making in uncertain dynamicenvironments. However, the solutions of MDPs are of limited practical use due to their sensitivityto distributional model parameters, which are typically unknown and have to be estimated by thedecision maker. To counter the detrimental effects of estimation errors, we consider robust MDPs...