Skill Discovery in Continuous Reinforcement Learning Domains Using Skill Chaining
Abstract
We introduce skill chaining, a skill discovery method for reinforcement learning agents in continuous domains, that builds chains of skills leading to an end-of-task reward. We demonstrate experimentally that it creates skills that result in performance benefits in a challenging continuous domain.
Cite
Text
Konidaris and Barto. "Skill Discovery in Continuous Reinforcement Learning Domains Using Skill Chaining." Neural Information Processing Systems, 2009.Markdown
[Konidaris and Barto. "Skill Discovery in Continuous Reinforcement Learning Domains Using Skill Chaining." Neural Information Processing Systems, 2009.](https://mlanthology.org/neurips/2009/konidaris2009neurips-skill/)BibTeX
@inproceedings{konidaris2009neurips-skill,
title = {{Skill Discovery in Continuous Reinforcement Learning Domains Using Skill Chaining}},
author = {Konidaris, George and Barto, Andrew G.},
booktitle = {Neural Information Processing Systems},
year = {2009},
pages = {1015-1023},
url = {https://mlanthology.org/neurips/2009/konidaris2009neurips-skill/}
}