Intrinsically Motivated Reinforcement Learning
Abstract
Psychologists call behavior intrinsically motivated when it is engaged in for its own sake rather than as a step toward solving a specific problem of clear practical value. But what we learn during intrinsically motivated behavior is essential for our development as competent autonomous en- tities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated reinforcement learning aimed at allowing arti- ficial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy.
Cite
Text
Chentanez et al. "Intrinsically Motivated Reinforcement Learning." Neural Information Processing Systems, 2004.Markdown
[Chentanez et al. "Intrinsically Motivated Reinforcement Learning." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/chentanez2004neurips-intrinsically/)BibTeX
@inproceedings{chentanez2004neurips-intrinsically,
title = {{Intrinsically Motivated Reinforcement Learning}},
author = {Chentanez, Nuttapong and Barto, Andrew G. and Singh, Satinder P.},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1281-1288},
url = {https://mlanthology.org/neurips/2004/chentanez2004neurips-intrinsically/}
}