In this work we introduce new approximate similarity relations that are shown to be key for policy (or control) synthesis over general Markov decision processes. The models of interest are discrete-time Markov decision processes, endowed with uncountably-infinite state spaces and metric output (or observation) spaces. The new relations, underpinned by the use of metrics, allow in particular for a useful trade-off between deviations over probability distributions on states, and distances between model outputs. We show that the new probabilistic similarity relations can be effectively employed over general Markov decision processes for verification purposes, and specifically for control refinement from abstract models.
|Title of host publication||Quantitative evaluation of systems, QEST 2016|
|Subtitle of host publication||13th International Conference, QEST 2016, Quebec City, QC, Canada, August 23-25, 2016, Proceedings|
|Editors||G. Agha, B. Van Houdt|
|Place of Publication||Dordrecht|
|Publication status||Published - 2016|
|Name||Lecture Notes in Computer Science|