Encoding and Combining Knowledge to Speed up Reinforcement Learning
Abstract
Reinforcement learning algorithms typically require too many `trial-and-error' experiences before reaching a desirable behaviour. A considerable amount of ongoing research is focused on speeding up this learning process by using external knowledge. We contribute in several ways, proposing novel approaches to transfer learning and learning from demonstration, as well as an ensemble approach to combine knowledge from various sources.
Cite
Text
Brys. "Encoding and Combining Knowledge to Speed up Reinforcement Learning." International Joint Conference on Artificial Intelligence, 2015.Markdown
[Brys. "Encoding and Combining Knowledge to Speed up Reinforcement Learning." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/brys2015ijcai-encoding/)BibTeX
@inproceedings{brys2015ijcai-encoding,
title = {{Encoding and Combining Knowledge to Speed up Reinforcement Learning}},
author = {Brys, Tim},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2015},
pages = {4347-4348},
url = {https://mlanthology.org/ijcai/2015/brys2015ijcai-encoding/}
}