Value-Directed Compression of POMDPs

Abstract

We examine the problem of generating state-space compressions of POMDPs in a way that minimally impacts decision quality. We analyze the impact of compres- sions on decision quality, observing that compressions that allow accurate policy evaluation (prediction of expected future reward) will not affect decision qual- ity. We derive a set of sufficient conditions that ensure accurate prediction in this respect, illustrate interesting mathematical properties these confer on lossless lin- ear compressions, and use these to derive an iterative procedure for finding good linear lossy compressions. We also elaborate on how structured representations of a POMDP can be used to find such compressions.

Cite

Text

Poupart and Boutilier. "Value-Directed Compression of POMDPs." Neural Information Processing Systems, 2002.

Markdown

[Poupart and Boutilier. "Value-Directed Compression of POMDPs." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/poupart2002neurips-valuedirected/)

BibTeX

@inproceedings{poupart2002neurips-valuedirected,
  title     = {{Value-Directed Compression of POMDPs}},
  author    = {Poupart, Pascal and Boutilier, Craig},
  booktitle = {Neural Information Processing Systems},
  year      = {2002},
  pages     = {1579-1586},
  url       = {https://mlanthology.org/neurips/2002/poupart2002neurips-valuedirected/}
}