A novel orthogonal NMF-based belief compression for POMDPs

Xin Li*, Kwok Wai CHEUNG, Jiming LIU, Zhili Wu

*Corresponding author for this work

Research output: Contribution to conferenceConference paperpeer-review

15 Citations (Scopus)


High dimensionality of POMDP's belief state space is one major cause that makes the underlying optimal policy computation intractable. Belief compression refers to the methodology that projects the belief state space to a low-dimensional one to alleviate the problem. In this paper, we propose a novel orthogonal non-negative matrix factorization (O-NMF) for the projection. The proposed O-NMF not only factors the belief state space by minimizing the reconstruction error, but also allows the compressed POMDP formulation to be efficiently computed (due to its orthogonality) in a value-directed manner so that the value function will take same values for corresponding belief states in the original and compressed state spaces. We have tested the proposed approach using a number of benchmark problems and the empirical results confirms its effectiveness in achieving substantial computational cost saving in policy computation.

Original languageEnglish
Number of pages8
Publication statusPublished - 2007
Event24th International Conference on Machine Learning, ICML 2007 - Corvalis, OR, United States
Duration: 20 Jun 200724 Jun 2007


Conference24th International Conference on Machine Learning, ICML 2007
Country/TerritoryUnited States
CityCorvalis, OR

Scopus Subject Areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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