Logic + Reinforcement Learning + Deep Learning: A Survey

Andreas Bueff, Vaishak Belle

2023

Abstract

Reinforcement learning has made significant strides in recent years, including in the development of Atari and Go-playing agents. It is now widely acknowledged that logical syntax adds considerable flexibility in both the modelling of domains as well as the interpretability of domains. In this survey paper, we cover the fundamentals of how logic, reinforcement learning, and deep learning can be unified, with some ideas for future work.

Paper Citation

in Harvard Style

Bueff A. and Belle V. (2023). Logic + Reinforcement Learning + Deep Learning: A Survey. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 713-722. DOI: 10.5220/0011746300003393

in Bibtex Style

@conference{icaart23,
author={Andreas Bueff and Vaishak Belle},
title={Logic + Reinforcement Learning + Deep Learning: A Survey},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={713-722},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011746300003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Logic + Reinforcement Learning + Deep Learning: A Survey
SN - 978-989-758-623-1
AU - Bueff A.
AU - Belle V.
PY - 2023
SP - 713
EP - 722
DO - 10.5220/0011746300003393