Debugging of Markov Decision Processes (MDPs) Models

Debugging of Markov Decision Processes (MDPs) Models

In model checking, a counterexample is considered as a valuable tool for debugging. In Probabilistic Model Checking (PMC), counterexample generation has a quantitative aspect. The counterexample in PMC is a set of paths in which a path formula holds, and their accumulative probability mass violates the probability threshold. However, understanding the counterexample is not an easy task. In this...

MDPs Semi - Markov decision processes Hidden Markov models Partially observable SMDPs Hierarchical HMMs

Ordinal Decision Models for Markov Decision Processes

Setting the values of rewards in Markov decision processes (MDP) may be a difficult task. In this paper, we consider two ordinal decision models for MDPs where only an order is known over rewards. The first one, which has been proposed recently in MDPs [23], defines preferences with respect to a reference point. The second model, which can been viewed as the dual approach of the first one, is b...

Bayesian Models of Nonstationary Markov Decision Processes

Standard reinforcement learning algorithms generate polices that optimize expected future rewards in a priori unknown domains, but they assume that the domain does not change over time. Prior work cast the reinforcement learning problem as a Bayesian estimation problem, using experience data to condition a probability distribution over domains. In this paper we propose an elaboration of the typ...

Bounded Parameter Markov Decision Processes Bounded Parameter Markov Decision Processes

In this paper, we introduce the notion of a bounded parameter Markov decision process as a generalization of the traditional exact MDP. A bounded parameter MDP is a set of exact MDPs speciied by giving upper and lower bounds on transition probabilities and rewards (all the MDPs in the set share the same state and action space). Bounded parameter MDPs can be used to represent variation or uncert...