Representation Learning for Context-Dependent Decision-Making
Abstract
Humans are capable of adjusting to changing environments flexibly and quickly. Empirical evidence has revealed that representation learning plays a crucial role in endowing humans with such a capability. Inspired by this observation, we study representation learning in the sequential decision-making scenario with contextual changes. We propose an online algorithm that is able to learn and transfer context-dependent representations and show that it significantly outperforms the existing ones that do not learn representations adaptively. As a case study, we apply our algorithm to the Wisconsin Card Sorting Task, a well-established test for the mental flexibility of humans in sequential decision-making. By comparing our algorithm with the standard Q-learning and Deep-Q learning algorithms, we demonstrate the benefits of adaptive representation learning.
Cite
@article{arxiv.2205.05820,
title = {Representation Learning for Context-Dependent Decision-Making},
author = {Yuzhen Qin and Tommaso Menara and Samet Oymak and ShiNung Ching and Fabio Pasqualetti},
journal= {arXiv preprint arXiv:2205.05820},
year = {2022}
}
Comments
3 Figures