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Abstraction for Deep Reinforcement Learning

Research article (Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022) · cited 14× · AI/ML
academia openalex:W4285606041
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Abstraction for Deep Reinforcement Learning

Summary

Abstraction for Deep Reinforcement Learning is a scholarly article[1].

Key Facts

  • Abstraction for Deep Reinforcement Learning's instance of is recorded as scholarly article[2].

Related Entities

Melanie Mitchell Murray Shanahan

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Abstraction for Deep Reinforcement Learning. Retrieved May 24, 2026, from https://4ort.xyz/entity/abstraction-for-deep-reinforcement-learning
MLA “Abstraction for Deep Reinforcement Learning.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/abstraction-for-deep-reinforcement-learning.
BibTeX @misc{4ortxyz_abstraction-for-deep-reinforcement-learning_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Abstraction for Deep Reinforcement Learning}}, year = {2026}, url = {https://4ort.xyz/entity/abstraction-for-deep-reinforcement-learning}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Abstraction for Deep Reinforcement Learning — https://4ort.xyz/entity/abstraction-for-deep-reinforcement-learning (retrieved 2026-05-24)

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