Efficient Nonlinear Control with Actor-Tutor Architecture
Abstract
A new reinforcement learning architecture for nonlinear control is proposed. A direct feedback controller, or the actor, is trained by a value-gradient based controller, or the tutor. This architecture enables both efficient use of the value function and simple computa(cid:173) tion for real-time implementation. Good performance was verified in multi-dimensional nonlinear control tasks using Gaussian soft(cid:173) max networks.
Cite
Text
Doya. "Efficient Nonlinear Control with Actor-Tutor Architecture." Neural Information Processing Systems, 1996.Markdown
[Doya. "Efficient Nonlinear Control with Actor-Tutor Architecture." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/doya1996neurips-efficient/)BibTeX
@inproceedings{doya1996neurips-efficient,
title = {{Efficient Nonlinear Control with Actor-Tutor Architecture}},
author = {Doya, Kenji},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {1012-1018},
url = {https://mlanthology.org/neurips/1996/doya1996neurips-efficient/}
}