The Complexity of Synchronizing Markov Decision Processes
Laurent Doyen, Thierry Massart, Mahsa Shirmohammadi

TL;DR
This paper studies the complexity and decidability of various synchronization modes in Markov decision processes, providing tight bounds and strategy requirements for ensuring probability distributions meet specific synchronization criteria.
Contribution
It introduces a comprehensive framework for analyzing synchronization in MDPs, establishing complexity bounds and strategy memory requirements for different synchronizing modes.
Findings
Decidability results for all synchronizing modes.
PSPACE-completeness for eventually and weakly synchronizing.
PTIME-completeness for always and strongly synchronizing.
Abstract
We consider Markov decision processes (MDP) as generators of sequences of probability distributions over states. A probability distribution is p-synchronizing if the probability mass is at least p in a single state, or in a given set of states. We consider four temporal synchronizing modes: a sequence of probability distributions is always p-synchronizing, eventually p-synchronizing, weakly p-synchronizing, or strongly p-synchronizing if, respectively, all, some, infinitely many, or all but finitely many distributions in the sequence are p-synchronizing. For each synchronizing mode, an MDP can be (i) sure winning if there is a strategy that produces a 1-synchronizing sequence; (ii) almost-sure winning if there is a strategy that produces a sequence that is, for all epsilon > 0, a (1-epsilon)-synchronizing sequence; (iii) limit-sure winning if for all epsilon > 0, there is a strategy…
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