Interval Methods for Uncertain Markov Decision Processes
نویسندگان
چکیده
In this paper, the average cases of Markov decision processes with uncertainty is considered. That is, a controlled Markov set-chain model with a finite state and action space is developed by an interval arithmetic analysis, and we will find a Pareto optimal policy which maximizes the average expected rewards over all stationary policies under a new partial order. The Pareto optimal policies is characterized by a maximal solution of an optimality equation which is derived from the model. Also, a maximin policy is obtained and shown to be Pareto-optimal. A numerical example is given.
منابع مشابه
Probabilistic Verification of Uncertain Systems Using Bounded-Parameter Markov Decision Processes
Verification of probabilistic systems is usually based on variants of Markov processes. For systems with continuous dynamics, Markov processes are generated using discrete approximation methods. These methods assume an exact model of the dynamic behavior. However, realistic systems operate in the presence of uncertainty and variability and they are described by uncertain models. In this paper, ...
متن کاملAccelerated decomposition techniques for large discounted Markov decision processes
Many hierarchical techniques to solve large Markov decision processes (MDPs) are based on the partition of the state space into strongly connected components (SCCs) that can be classified into some levels. In each level, smaller problems named restricted MDPs are solved, and then these partial solutions are combined to obtain the global solution. In this paper, we first propose a novel algorith...
متن کاملThe Design of Inverse Network DEA Model for Measuring the Bullwhip Effect in Supply Chains with Uncertain Demands
Two different bullwhip effects with equal scores may have different sensitivities and production patterns. As a result, the difference between these two seemingly equal scores has been ignored in previous methods (such as frequency response and moving average). So, the present study constructs a model of Inverse Network Data Envelopment Analysis, to introduce the relative and interval scores of...
متن کاملReachability analysis of uncertain systems using bounded-parameter Markov decision processes
Verification of reachability properties for probabilistic systems is usually based on variants of Markov processes. Current methods assume an exact model of the dynamic behavior and are not suitable for realistic systems that operate in the presence of uncertainty and variability. This research note extends existing methods for Bounded-parameter Markov Decision Processes (BMDPs) to solve the re...
متن کاملIncreasing Scalability in Algorithms for Centralized and Decentralized Partially Observable Markov Decision Processes: Efficient Decision-Making and Coordination in Uncertain Environments
INCREASING SCALABILITY IN ALGORITHMS FOR CENTRALIZED AND DECENTRALIZED PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES: EFFICIENT DECISION-MAKING AND COORDINATION IN UNCERTAIN ENVIRONMENTS
متن کامل