Patching Approximate Solutions in Reinforcement Learning
Abstract
This paper introduces an approach to improving an approximate solution in reinforcement learning by augmenting it with a small overriding patch. Many approximate solutions are smaller and easier to produce than a flat solution, but the best solution within the constraints of the approximation may fall well short of global optimality. We present a technique for efficiently learning a small patch to reduce this gap. Empirical evaluation demonstrates the effectiveness of patching, producing combined solutions that are much closer to global optimality.
Cite
Text
Kim and Uther. "Patching Approximate Solutions in Reinforcement Learning." European Conference on Machine Learning, 2006. doi:10.1007/11871842_27Markdown
[Kim and Uther. "Patching Approximate Solutions in Reinforcement Learning." European Conference on Machine Learning, 2006.](https://mlanthology.org/ecmlpkdd/2006/kim2006ecml-patching/) doi:10.1007/11871842_27BibTeX
@inproceedings{kim2006ecml-patching,
title = {{Patching Approximate Solutions in Reinforcement Learning}},
author = {Kim, Min Sub and Uther, William T. B.},
booktitle = {European Conference on Machine Learning},
year = {2006},
pages = {258-269},
doi = {10.1007/11871842_27},
url = {https://mlanthology.org/ecmlpkdd/2006/kim2006ecml-patching/}
}