Transfer of Learning Across Compositions of Sequentail Tasks
Abstract
Most “weak― learning algorithms, including reinforcement learning methods, have been applied on tasks with single goals. The effort to build more sophisticated learning systems that operate in complex environments will require the ability to handle multiple goals. Methods that allow transfer of learning will play a crucial role in learning systems that support multiple goals. In this paper I describe a class of multiple tasks that represents a subset of routine animal activity. I present a new learning algorithm and an architecture that allows transfer of learning by the “sharing― of solutions to the common parts of multiple tasks. A proof of the algorithm is also provided.
Cite
Text
Singh. "Transfer of Learning Across Compositions of Sequentail Tasks." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50072-6Markdown
[Singh. "Transfer of Learning Across Compositions of Sequentail Tasks." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/singh1991icml-transfer/) doi:10.1016/B978-1-55860-200-7.50072-6BibTeX
@inproceedings{singh1991icml-transfer,
title = {{Transfer of Learning Across Compositions of Sequentail Tasks}},
author = {Singh, Satinder P.},
booktitle = {International Conference on Machine Learning},
year = {1991},
pages = {348-352},
doi = {10.1016/B978-1-55860-200-7.50072-6},
url = {https://mlanthology.org/icml/1991/singh1991icml-transfer/}
}