The DOI matches its Crossref record.
Reinforcement Learning: An Introduction
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Actor-Critic methods are a widely used class of reinforcement learning (RL) algorithms that integrate both policy-based and value-based approaches [80,81].”
From Adaptive Information Modulation: Designing Governance Mechanisms for Multi-Agent Artificial Intelligence Systems (2026) · cites this paper as [80]
In the citing paper
“Such attributions of causality are a precondition for learning,2”
From Two prefrontal-subcortical circuits estimate controllability and reflect its impact on learning (2026) · cites this paper as [3]
In the citing paper
“Another distinguishing aspect is that, in this framework, actions are triggered by goals through prediction errors, whereas in most reinforcement learning schemes, responses are triggered by stimuli via associative links – and prediction errors have only a role in learning (e.g., using Temporal Difference learning) (Sutton and Barto, 1998).”
From Active Inference, homeostatic regulation and adaptive behavioural control (2015) · cites this paper as Sutton and Barto, 1998
In the citing paper
“and interpreted in the framework of reinforcement learning (RL) theory4-5.”
From A reservoir of time constants for memory traces in cortical neurons (2011) · cites this paper as [5]
Citations
Cited by
Sorted by citations
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Metadata
- Authors
- Sutton, R. S., & Barto, A. G.
- Affiliations
- MIT Press
- ORCID
- Sutton, R.S.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1998-09
- Volume
- 9
- Issue
- 5
- Pages
- 1054-1054
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W4214717370
- Cited by
- 27,225
- References
- 0
- Citations by year
- 2026: 818 · 2025: 2403 · 2024: 2277 · 2023: 2382 · 2022: 2316 · 2021: 2022 · 2020: 1875 · 2019: 1731 · 2018: 1444 · 2017: 1058 · 2016: 852 · 2015: 782 · 2014: 892 · 2013: 817 · 2012: 816
- FWCI
- 95.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Neural Networks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- reinforcement learning, learning from interaction, optimal control, Markov decision process, policy optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
