Reinforcement Learning for Call Admission Control and Routing in Integrated Service Networks
Abstract
We provide a model of the standard watermaze task, and of a more challenging task involving novel platform locations, in which rats exhibit one-trial learning after a few days of training. The model uses hippocampal place cells to support reinforcement learning, and also, in an integrated manner, to build and use allocentric coordinates.
Cite
Text
Marbach et al. "Reinforcement Learning for Call Admission Control and Routing in Integrated Service Networks." Neural Information Processing Systems, 1997.Markdown
[Marbach et al. "Reinforcement Learning for Call Admission Control and Routing in Integrated Service Networks." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/marbach1997neurips-reinforcement/)BibTeX
@inproceedings{marbach1997neurips-reinforcement,
title = {{Reinforcement Learning for Call Admission Control and Routing in Integrated Service Networks}},
author = {Marbach, Peter and Mihatsch, Oliver and Schulte, Miriam and Tsitsiklis, John N.},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {922-928},
url = {https://mlanthology.org/neurips/1997/marbach1997neurips-reinforcement/}
}