A Review on Sample Efficiency Improvement Methods in Reinforcement Learning
Abstract
Reinforcement Learning (RL) faces a significant challenge in sample efficiency due to the need for costly interactions with the environment in real-world applications. This review, by examining 21 main papers and 11 supporting papers, argues that sample efficiency is not merely about reducing the number of interactions, but rather extracting the maximum useful information from each real interaction. In this paper, the reviewed methods are categorized into five levels: structural, policy-temporal, task-preference, data-driven, and semantic. This categorization is a proposal by the authors. The integrated conceptual framework of "Multi-Scale Information Density," along with four levers and a gradual trust-opening schedule, provides a framework for selecting and combining methods. In addition to the categorization, the paper presents a comparative table of 21 studies, three illustrative application scenarios, and a conceptual proposal for quantitatively measuring sample efficiency. This framework has not yet been tested in a single experimental setting.
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BibTeX
@misc{koshaly2026review,
title = {{A Review on Sample Efficiency Improvement Methods in Reinforcement Learning}},
author = {Ali Honarmand Koshaly and Ehsan Najafzadegan and Amir Lashgari Nejad and Farzaneh Rahmani},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23539271},
url = {https://zenodo.org/records/23539271},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Review on Sample Efficiency Improvement Methods in Reinforcement Learning
AU - Ali Honarmand Koshaly
AU - Ehsan Najafzadegan
AU - Amir Lashgari Nejad
AU - Farzaneh Rahmani
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.5281/zenodo.23539271
UR - https://zenodo.org/records/23539271
AB - Reinforcement Learning (RL) faces a significant challenge in sample efficiency due to the need for costly interactions with the environment in real-world applications. This review, by examining 21 main papers and 11 supporting papers, argues that sample efficiency is not merely about reducing the number of interactions, but rather extracting the maximum useful information from each real interaction. In this paper, the reviewed methods are categorized into five levels: structural, policy-temporal, task-preference, data-driven, and semantic. This categorization is a proposal by the authors. The integrated conceptual framework of "Multi-Scale Information Density," along with four levers and a gradual trust-opening schedule, provides a framework for selecting and combining methods. In addition to the categorization, the paper presents a comparative table of 21 studies, three illustrative application scenarios, and a conceptual proposal for quantitatively measuring sample efficiency. This framework has not yet been tested in a single experimental setting.
ER -