Author

Chad Kessens

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#artificial intelligence Jan 2026

Toward Learning POMDPs Beyond Full-Rank Actions and State Observability

It is shown how Predictive State Representations learn POMDP matrices up to a similarity transform, and this transform may be estimated via tensor decomposition methods, and it is shown that learning a POMDP beyond a partition of states is impossible from sequential data.

Seiji Shaw, Travis Manderson, Chad Kessens et al. · arXiv.org · 1 citation