Human-level control through deep reinforcement learning
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- Daan Wierstra · David Silver · Demis Hassabis · Ioannis Antonoglou · Koray Kavukcuoglu · Shane Legg
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- Human-level control through deep reinforcement learning Crossref · published 2015 · Crossref record of the journal version: Nature, vol. 518, no. 7540, pp. 529-533 (DOI 10.1038/nature14236), retrieved for the candidate pack on 2026-10-28. The date is the publisher-deposited date, kept at year precision; it was not checked against the issue.
- Human-level control through deep reinforcement learning Nature · published 2015 · Publisher’s article page (abstract only; the article text is not open). Year from Crossref. Retrieved for the candidate pack on 2026-10-28 (SHA-256 171622170256d1bfa2a5675a8b4c2854d6f38d6c009922cc1b243729b3269e87). The author list is the byline as printed on the first page.
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- Author: Daan WierstraDaan Wierstra is a credited author of Human-level control through deep reinforcement learning.2015 · publication · Stated in source · Supported Inspect claim & evidence ↗Daan Wierstra: person page →
- Author: David SilverDavid Silver is a credited author of Human-level control through deep reinforcement learning.2015 · publication · Stated in source · Supported Inspect claim & evidence ↗David Silver: person page →
- Author: Demis HassabisDemis Hassabis is a credited author of Human-level control through deep reinforcement learning.2015 · publication · Stated in source · Supported Inspect claim & evidence ↗Demis Hassabis: person page →
- Author: Ioannis AntonoglouIoannis Antonoglou is a credited author of Human-level control through deep reinforcement learning.2015 · publication · Stated in source · Supported Inspect claim & evidence ↗Ioannis Antonoglou: person page →
- Author: Koray KavukcuogluKoray Kavukcuoglu is a credited author of Human-level control through deep reinforcement learning.2015 · publication · Stated in source · Supported Inspect claim & evidence ↗Koray Kavukcuoglu: person page →
- Author: Shane LeggShane Legg is a credited author of Human-level control through deep reinforcement learning.2015 · publication · Stated in source · Supported Inspect claim & evidence ↗Shane Legg: person page →
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Cited by · 5
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- Alignment for Advanced Machine Learning SystemsAlignment for Advanced Machine Learning Systems cites Human-level control through deep reinforcement learning.2016 · publication · Stated in source · Supported Inspect claim & evidence ↗Alignment for Advanced Machine Learning Systems: document page →
- Concrete Problems in AI SafetyConcrete Problems in AI Safety cites Human-level control through deep reinforcement learning.2016-06-21 · publication · Stated in source · Supported Inspect claim & evidence ↗Concrete Problems in AI Safety: document page →
- Deep reinforcement learning from human preferencesDeep reinforcement learning from human preferences cites Human-level control through deep reinforcement learning.2017-06-12 · publication · Stated in source · Supported Inspect claim & evidence ↗Deep reinforcement learning from human preferences: document page →
- Proximal Policy Optimization AlgorithmsProximal Policy Optimization Algorithms cites Human-level control through deep reinforcement learning.2017-07-20 · publication · Stated in source · Supported Inspect claim & evidence ↗Proximal Policy Optimization Algorithms: document page →
- Scalable agent alignment via reward modeling: a research directionScalable agent alignment via reward modeling: a research direction cites Human-level control through deep reinforcement learning.2018-11-19 · publication · Stated in source · Supported Inspect claim & evidence ↗Scalable agent alignment via reward modeling: a research direction: document page →
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- In its own wordsThe paper presents an agent, the deep Q-network, that learns policies directly from high-dimensional sensory inputs through end-to-end reinforcement learning.“Here we use recent advances in training deep neural networks … to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning.” Nature article page, abstract2015 · publication · Stated in source · Supported Inspect claim & evidence ↗
- In its own words · includes its own priority claimThe paper states that its agent is the first artificial agent capable of learning to excel at a diverse array of challenging tasks.“This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.” Nature article page, abstract2015 · publication · Stated in source · Supported Inspect claim & evidence ↗