Learning Predictive Representations from a History
Abstract
Predictive State Representations (PSRs) have shown a great deal of promise as an alternative to Markov models. However, learning a PSR from a single stream of data generated from an environment remains a challenge. In this work, we present a formalism of PSRs and the domains they model. This formalization suggests an algorithm for learning PSRs that will (almost surely) converge to a globally optimal model given sufficient training data.
Cite
Text
Wiewiora. "Learning Predictive Representations from a History." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102473Markdown
[Wiewiora. "Learning Predictive Representations from a History." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/wiewiora2005icml-learning/) doi:10.1145/1102351.1102473BibTeX
@inproceedings{wiewiora2005icml-learning,
title = {{Learning Predictive Representations from a History}},
author = {Wiewiora, Eric},
booktitle = {International Conference on Machine Learning},
year = {2005},
pages = {964-971},
doi = {10.1145/1102351.1102473},
url = {https://mlanthology.org/icml/2005/wiewiora2005icml-learning/}
}