Logic + Reinforcement Learning + Deep Learning: A Survey

Andreas Bueff; Vaishak Belle

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Paper

Logic + Reinforcement Learning + Deep Learning: A Survey

Topics: Knowledge Representation and Reasoning; Machine Learning; Planning and Scheduling

In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, 713-722, 2023 , Lisbon, Portugal

Authors: Andreas Bueff and Vaishak Belle

Affiliation: University of Edinburgh, U.K.

Keyword(s): Logic-Based Reinforcement Learning.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:

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; ISSN 2184-433X, SciTePress, pages 713-722. DOI: 10.5220/0011746300003393

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Bueff, A.
AU - Belle, V.
PY - 2023
SP - 713
EP - 722
DO - 10.5220/0011746300003393
PB - SciTePress