An Overview of Environmental Features That Impact Deep Reinforcement Learning in Sparse-Reward Domains
Abstract
Deep reinforcement learning has achieved impressive results in recent years; yet, it is still severely troubled by environments showcasing sparse rewards. On top of that, not all sparse-reward environments are created equal, i.e., they can differ in the presence or absence of various features, with many of them having a great impact on learning. In light of this, the present work puts together a literature compilation of such environmental features, covering particularly those that have been taken advantage of and those that continue to pose a challenge. We expect this effort to provide guidance to researchers for assessing the generality of their new proposals and to call their attention to issues that remain unresolved when dealing with sparse rewards.
Cite
Text
Ocana et al. "An Overview of Environmental Features That Impact Deep Reinforcement Learning in Sparse-Reward Domains." Journal of Artificial Intelligence Research, 2023. doi:10.1613/JAIR.1.14390Markdown
[Ocana et al. "An Overview of Environmental Features That Impact Deep Reinforcement Learning in Sparse-Reward Domains." Journal of Artificial Intelligence Research, 2023.](https://mlanthology.org/jair/2023/ocana2023jair-overview/) doi:10.1613/JAIR.1.14390BibTeX
@article{ocana2023jair-overview,
title = {{An Overview of Environmental Features That Impact Deep Reinforcement Learning in Sparse-Reward Domains}},
author = {Ocana, Jim Martin Catacora and Capobianco, Roberto and Nardi, Daniele},
journal = {Journal of Artificial Intelligence Research},
year = {2023},
pages = {1181-1218},
doi = {10.1613/JAIR.1.14390},
volume = {76},
url = {https://mlanthology.org/jair/2023/ocana2023jair-overview/}
}