• tag:aim-pro.eu,2026:oer/f9cf3be0033f
• https://arxiv.org/abs/2309.01038
• arxiv:2309.01038
• doi:10.1109/TAI.2023.3311428

This engine's id, the source URI, and any persistent identifier the source published.

• Neurosymbolic Reinforcement Learning and Planning: A Survey

The title of this resource, not of the record or repository it came from.

• dcmitype:Text
• preprint

A DCMI Type term for the form held, plus the source's own genre label as declared.

• K. Acharya
• W. Raza
• C. M. J. M. Dourado
• A. Velasquez
• H. Song

Only agents the source named as creators. A repository owner is not made an author by default.

• The area of Neurosymbolic Artificial Intelligence (Neurosymbolic AI) is rapidly developing and has become a popular research topic, encompassing sub-fields such as Neurosymbolic Deep Learning (Neurosymbolic DL) and Neurosymbolic Reinforcement Learning (Neurosymbolic RL). Compared to traditional learning methods, Neurosymbolic AI offers significant advantages by simplifying complexity and providing transparency and explainability. Reinforcement Learning(RL), a long-standing Artificial Intelligence(AI) concept that mimics human behavior using rewards and punishment, is a fundamental component of Neurosymbolic RL, a recent integration of the two fields that has yielded promising results. The aim of this paper is to contribute to the emerging field of Neurosymbolic RL by conducting a literature survey. Our evaluation focuses on the three components that constitute Neurosymbolic RL: neural, symbolic, and RL. We categorize works based on the role played by the neural and symbolic parts in RL, into three taxonomies:Learning for Reasoning, Reasoning for Learning and Learning-Reasoning. These categories are further divided into sub-categories based on their applications. Furthermore, we analyze the RL components of each research work, including the state space, action space, policy module, and RL algorithm. Additionally, we identify research opportunities and challenges in various applications within this dynamic field.

The abstract, and the depositor's additional notes after it when the source has a field for them.

• cs.AI (arxiv)
• cs.LG (arxiv)

Declared keywords and taxonomy terms, each carrying its scheme.

• en

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• http://creativecommons.org/licenses/by/4.0/

The licence document, as a URI where the gate resolved one.

• CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive

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• open

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One value per form held — the original and the extracted text.

• 94694 bytes (extracted text)

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• 2023-09-02

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• arXiv:2104.06890
• arXiv:1711.03902
• arXiv:2210.15889
• arXiv:2111.08164
• arXiv:2209.12618
• arXiv:1701.07274
• arXiv:2302.05209
• arXiv:1906.03926
• doi:10.1145/3477600
• arXiv:1702.07800
• arXiv:1312.5602
• arXiv:1509.02971
• and 21 more, every one of them in the export

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• Retrieved from arXiv on 2026-10-09 in response to the search string “(all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"AI concepts" OR all:"types of AI" OR all:"AI fundamentals" OR all:"recognizing AI" OR all:"recognising AI" OR all:"general versus narrow AI" OR all:"narrow AI" OR all:"general AI" OR all:"machine intelligence" OR all:"AI strengths and weaknesses" OR all:"traditional software" OR all:"rule-based systems" OR all:"introduction to AI" OR all:"introduction to artificial intelligence" OR all:"artificial intelligence introduction" OR all:"AI primer" OR all:"foundations of artificial intelligence" OR all:"overview of AI" OR all:"understanding AI" OR all:"history of AI" OR all:"AI essentials" OR all:"AI terminology" OR all:"metaphors for AI" OR all:"AI fundamental concepts" OR all:"AI key concepts" OR all:"philosophy of AI" OR all:"critical AI literacy")”. arXiv served the resource and is not asserted to be its publisher or author.
• Text extracted from latexml-html to Markdown by arxiv-html; the original is retained unchanged beside it.

Where it was collected from, what was converted, and what container it came out of — the custody statements that would otherwise be mistaken for authorship.

• URI: tag:aim-pro.eu,2026:oer/f9cf3be0033f
• URI: https://arxiv.org/abs/2309.01038
• ARXIV: 2309.01038
• DOI: 10.1109/TAI.2023.3311428

This engine's identifier for the resource, the source URI, and any persistent identifier the source published.

• Neurosymbolic Reinforcement Learning and Planning: A Survey

• en

The language of the content, as the language gate read it off the converted text. The language the source declared is kept separately.

• The area of Neurosymbolic Artificial Intelligence (Neurosymbolic AI) is rapidly developing and has become a popular research topic, encompassing sub-fields such as Neurosymbolic Deep Learning (Neurosymbolic DL) and Neurosymbolic Reinforcement Learning (Neurosymbolic RL). Compared to traditional learning methods, Neurosymbolic AI offers significant advantages by simplifying complexity and providing transparency and explainability. Reinforcement Learning(RL), a long-standing Artificial Intelligence(AI) concept that mimics human behavior using rewards and punishment, is a fundamental component of Neurosymbolic RL, a recent integration of the two fields that has yielded promising results. The aim of this paper is to contribute to the emerging field of Neurosymbolic RL by conducting a literature survey. Our evaluation focuses on the three components that constitute Neurosymbolic RL: neural, symbolic, and RL. We categorize works based on the role played by the neural and symbolic parts in RL, into three taxonomies:Learning for Reasoning, Reasoning for Learning and Learning-Reasoning. These categories are further divided into sub-categories based on their applications. Furthermore, we analyze the RL components of each research work, including the state space, action space, policy module, and RL algorithm. Additionally, we identify research opportunities and challenges in various applications within this dynamic field.

The abstract, and the depositor's additional notes after it as a second LangString when the source has a field for them.

• cs.AI
• cs.LG

• v1

• K. Acharya — author
• W. Raza — author
• C. M. J. M. Dourado — author
• A. Velasquez — author
• H. Song — author

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• URI: tag:aim-pro.eu,2026:oer/f9cf3be0033f/record

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• AIM-PRO WP3 OER harvester (arXiv) — creator

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• LOMv1.0
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LOMv1.0, and the profile this was built against.

• en

• text/markdown

One value per form held: the original as the source published it, and the Markdown this engine extracted.

• 94694

Bytes. The original's, because the resource is the file and not our conversion of it.

• https://arxiv.org/abs/2309.01038

Where the source serves it.

• narrative text

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• no — free to use

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• yes — attribution required (CC-BY-4.0)

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• CC-BY-4.0 · http://creativecommons.org/licenses/by/4.0/. Conditions: adaptation permitted, attribution required.

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