@inproceedings{
lee2023uncertaintyaware,
title={Uncertainty-Aware Action Repeating Options},
author={Joongkyu Lee and Seung Joon Park and Yunhao Tang and Min-hwan Oh},
booktitle={NeurIPS 2023 Workshop on Generalization in Planning},
year={2023},
url={https://openreview.net/forum?id=225zToqXbL}
}
In reinforcement learning, employing temporal abstraction within the action space is a prevalent strategy for simplifying policy learning through temporally-extended actions. 
Recently, algorithms that repeat a primitive action for a certain number of steps, a simple method to implement temporal abstraction in practice, have demonstrated better performance than traditional algorithms.
However, a significant drawback of earlier studies on action repetition is the potential for repeated sub-optimal actions to considerably degrade performance.
To tackle this problem, we introduce a new algorithm that employs ensemble methods to estimate uncertainty when extending an action. 
Our framework offers flexibility, allowing policies to either prioritize exploration or adopt an uncertainty-averse stance based on their specific needs.
We provide empirical results on various environments, highlighting the superior performance of our proposed method compared to other action-repeating algorithms. 
These results indicate that our uncertainty-aware strategy effectively counters the downsides of action repetition, enhancing policy learning efficiency.
Uncertainty-Aware Action Repeating Options
Joongkyu Lee Seung Joon Park Yunhao Tang Min-hwan Oh