2022-06-28-kosoy22a.md

abstract	booktitle	title	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Despite recent progress in reinforcement learning (RL), RL algorithms for exploration still remain an active area of research. Existing methods often focus on state-based metrics, which do not con-sider the underlying causal structures of the environment, and while recent research has begun to explore RL environments for causal learning, these environments primarily leverage causal information through causal inference or induction rather than exploration. In contrast, human children—some of the most proficient explorers—have been shown to use causal information to great benefit.In this work, we introduce a novel RL environment designed with a controllable causal structure, which allows us to evaluate exploration strategies used by both agents and children in a unified environment. In addition, through experimentation on both computation models and children, we demonstrate that there are significant differences between information-gain optimal RL exploration in causal environments and the exploration of children in the same environments. We leverage this new insight to lay the groundwork for future research into efficient exploration and disambiguation of causal structures for RL algorithms.	First Conference on Causal Learning and Reasoning	Learning Causal Overhypotheses through Exploration in Children and Computational Models	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	kosoy22a	0	Learning Causal Overhypotheses through Exploration in Children and Computational Models	390	406	390-406	390	false	Kosoy, Eliza and Liu, Adrian and Collins, Jasmine L and Chan, David and Hamrick, Jessica B and Ke, Nan Rosemary and Huang, Sandy and Kaufmann, Bryanna and Canny, John and Gopnik, Alison	given family Eliza Kosoy given family Adrian Liu given family Jasmine L Collins given family David Chan given family Jessica B Hamrick given family Nan Rosemary Ke given family Sandy Huang given family Bryanna Kaufmann given family John Canny given family Alison Gopnik	2022-06-28		Proceedings of the First Conference on Causal Learning and Reasoning	177	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v177/kosoy22a/kosoy22a.pdf