Reinforcement Learning: An Introduction
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Actor-Critic methods integrate policy-based and value-based approaches.
“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]
Attributions of causality are a precondition for learning.
“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 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.
“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
Neural correlates of reward valuation have been interpreted in the framework of reinforcement learning (RL) theory.
“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]
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- 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 ›
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