Qualitative Multi-Objective Reachability for Ordered Branching MDPs
Kousha Etessami, Emanuel Martinov

TL;DR
This paper develops algorithms to determine almost-sure and limit-sure multi-target reachability in Ordered Branching Markov Decision Processes, highlighting differences between achieving probability 1 exactly and arbitrarily close, with fixed-parameter tractability.
Contribution
It introduces two algorithms for multi-target reachability in OBMDPs, distinguishing between almost-sure and limit-sure cases, and analyzes their complexity and hardness.
Findings
Algorithms run in 2^{O(k)} * polynomial time for fixed k.
Almost-sure and limit-sure reachability are distinct in OBMDPs.
Deciding these properties is NP-hard when k is unbounded.
Abstract
We study qualitative multi-objective reachability problems for Ordered Branching Markov Decision Processes (OBMDPs), or equivalently context-free MDPs, building on prior results for single-target reachability on Branching Markov Decision Processes (BMDPs). We provide two separate algorithms for "almost-sure" and "limit-sure" multi-target reachability for OBMDPs. Specifically, given an OBMDP, , given a starting non-terminal, and given a set of target non-terminals of size , our first algorithm decides whether the supremum probability, of generating a tree that contains every target non-terminal in set , is . Our second algorithm decides whether there is a strategy for the player to almost-surely (with probability ) generate a tree that contains every target non-terminal in set . The two separate algorithms are needed: we show that indeed, in this…
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