The Efficient Learning of Multiple Task Sequences
Abstract
I present a modular network architecture and a learning algorithm based on incremental dynamic programming that allows a single learning agent to learn to solve multiple Markovian decision tasks (MDTs) with signif(cid:173) icant transfer of learning across the tasks. I consider a class of MDTs, called composite tasks, formed by temporally concatenating a number of simpler, elemental MDTs. The architecture is trained on a set of compos(cid:173) ite and elemental MDTs. The temporal structure of a composite task is assumed to be unknown and the architecture learns to produce a tempo(cid:173) ral decomposition. It is shown that under certain conditions the solution of a composite MDT can be constructed by computationally inexpensive modifications of the solutions of its constituent elemental MDTs.
Cite
Text
Singh. "The Efficient Learning of Multiple Task Sequences." Neural Information Processing Systems, 1991.Markdown
[Singh. "The Efficient Learning of Multiple Task Sequences." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/singh1991neurips-efficient/)BibTeX
@inproceedings{singh1991neurips-efficient,
title = {{The Efficient Learning of Multiple Task Sequences}},
author = {Singh, Satinder P.},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {251-258},
url = {https://mlanthology.org/neurips/1991/singh1991neurips-efficient/}
}