Toward Learning POMDPs Beyond Full-Rank Actions and State Observability

Abstract

-cross Abstract: We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms. We cast this problem as learning the parameters of a discrete Partially Observable Markov Decision Process (POMDP). The agent begins with knowledge of the POMDP's actions and observation spaces, but not its state space, transitions, or observation models. These properties must be constructed from a sequence of actions and observations. Spectral approaches to learning models of partially observable domains, such as Predictive State Representations (PSRs), learn representations of state that are sufficient to predict future outcomes. PSR models, however, do not have explicit transition and observation system models that can be used with different reward functions to solve different planning problems. Under a mild set of rankness assumptions on the products of transition and observation matrices, we show how PSRs learn POMDP matrices up to a similarity transform, and this transform may be estimated via tensor decomposition methods. Our method learns observation matrices and transition matrices up to a partition of states, where the states in a single partition have the same observation distributions corresponding to actions whose transition matrices are full-rank. Our numerical experiments suggest that explicit observation and transition likelihoods can be leveraged to generate new plans for different goals and reward functions after the model has been learned. We also show that learning a POMDP beyond a partition of states is impossible from sequential data by constructing two POMDPs that agree on all observation distributions but differ in their transition dynamics.

Authors 4

Seiji Shaw, Travis Manderson, Chad Kessens, Nicholas Roy

Specification

Official page

arxiv.org/abs/2601.18930

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Arxiv announce type

replace

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

arXiv id

2601.18930

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Categories

cs.LG, cs.AI, cs.RO

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

PDF

arxiv.org/pdf/2601.18930

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Primary category

cs.LG

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Published

11 Sept 2026

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

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