Parameter-Independent Strategies for pMDPs via POMDPs
QEST, pp. 53-70, 2018.
Markov Decision Processes (MDPs) are a popular class of models suitable for solving control decision problems in probabilistic reactive systems. We consider parametric MDPs (pMDPs) that include parameters in some of the transition probabilities to account for stochastic uncertainties of the environment such as noise or input disturbances.
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