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We study a subclass of POMDPs, called Deterministic POMDPs, that is characterized by deterministic actions and observations. These models do not provide the same generality of POMDPs yet they capture a number of interesting and challenging problems, and permit more efficient algorithms. Indeed, some of the recent work in planning is built around such assumptions mainly by the quest of amenable models more expressive than the classical deterministic models. We provide results about the fundamental properties of Deterministic POMDPs, their relation with AND/OR search problems and algorithms, and their computational complexity.
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Cited by 2 Pith papers
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A Model-free Biomimetics Algorithm for Deterministic Partially Observable Markov Decision Process
BIOMAP achieves the optimal reward on the Mask Cliff Walking benchmark by reconstructing the state graph from action vectors, but this hinges on an unstated assumption that states are geometric positions.
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