A Novel Framework for Uncertainty-Driven Adaptive Exploration
Abstract
Adaptive exploration methods propose ways to learn complex policies via alternating between exploration and exploitation. An important question for such methods is to determine the appropriate moment to switch between exploration and exploitation and vice versa. This is critical in domains that require the learning of long and complex sequences of actions. In this work, we present a generic adaptive exploration framework that employs uncertainty to address this important issue in a principled manner. Our framework includes previous adaptive exploration approaches as special cases. Moreover, we can incorporate in our framework any uncertainty-measuring mechanism of choice, for instance mechanisms used in intrinsic motivation or epistemic uncertainty-based exploration methods. We experimentally demonstrate that our framework gives rise to adaptive exploration strategies that outperform standard ones across several environments.
Cite
@article{arxiv.2509.03219,
title = {A Novel Framework for Uncertainty-Driven Adaptive Exploration},
author = {Leonidas Bakopoulos and Georgios Chalkiadakis},
journal= {arXiv preprint arXiv:2509.03219},
year = {2026}
}
Comments
This is an extended version (full paper + appendix) of the paper titled "A Novel Framework for Uncertainty-Driven Adaptive Exploration" accepted as a full paper at AAMAS 2026. The accepted paper can be found in https://openreview.net/forum?id=j5awxzdsU9