arXiv:2602.07473v3 Announce Type: replace
Abstract: We introduce posterior-deterministic POMDPs, and we show that for this class of POMDPs, the maximal probability of reaching a given set of states can be approximated up to arbitrary precision. A POMDP is posterior-deterministic if the next state can be uniquely determined by the current state, the action taken, and the observation received. In particular, once the current state is known, it remains known forever. The class contains, for instance, multiple-environment MDPs equipped with a prior distribution over the environments, quasi-deterministic POMDPs, and the episodic version of the classical Tiger POMDP (Kaelbling et al. 1998).
Abstract: We introduce posterior-deterministic POMDPs, and we show that for this class of POMDPs, the maximal probability of reaching a given set of states can be approximated up to arbitrary precision. A POMDP is posterior-deterministic if the next state can be uniquely determined by the current state, the action taken, and the observation received. In particular, once the current state is known, it remains known forever. The class contains, for instance, multiple-environment MDPs equipped with a prior distribution over the environments, quasi-deterministic POMDPs, and the episodic version of the classical Tiger POMDP (Kaelbling et al. 1998).
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