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A novel orthogonal NMF-based belief compression for POMDPs

A novel orthogonal NMF-based belief compression for POMDPs,10.1145/1273496.1273564,Xin Li,William Kwok-wai Cheung,Jiming Liu,Zhili Wu

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A novel orthogonal NMF-based belief compression for POMDPs   (Citations: 3)
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High dimensionality of POMDP's belief state space is one major cause that makes the underlying optimal policy computation in- tractable. 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 factor- ization (O-NMF) for the projection. The proposed O-NMF not only factors the be- lief state space by minimizing the reconstruc- tion error, but also allows the compressed POMDP formulation to be efficiently com- puted (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 ap- proach using a number of benchmark prob- lems and the empirical results confirms its effectiveness in achieving substantial compu- tational cost saving in policy computation.
Conference: International Conference on Machine Learning - ICML , pp. 537-544, 2007
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