Compressing POMDPs Using Locality Preserving Non-Negative Matrix Factorization

@inproceedings{Theocharous2010CompressingPU,
  title={Compressing POMDPs Using Locality Preserving Non-Negative Matrix Factorization},
  author={Georgios Theocharous and Sridhar Mahadevan},
  booktitle={AAAI},
  year={2010}
}
Partially Observable Markov Decision Processes (POMDPs) are a well-established and rigorous framework for sequential decision-making under uncertainty. POMDPs are well-known to be intractable to solve exactly, and there has been significant work on finding tractable approximation methods. One well-studied approach is to find a compression of the original… CONTINUE READING