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Reinforcement Learning: An Introduction

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·2005·IEEE Transactions on Neural Networks

DOI 10.1109/tnn.2004.842673

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Abstract

An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.

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Guggenmos, M., Wilbertz, G., Hebart, M. N., & Sterzer, P. (2016) · eLife · doi:10.7554/elife.13388 · Cited by 164

Venue: IEEE Transactions on Neural Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2005-01
Volume: 16
Issue: 1
Pages: 285-286
ISSN: 1045-9227 (ISSN-L)
DOI: 10.1109/tnn.2004.842673
OpenAlex: W2121863487
Cited by: 24,496
References: 66
FWCI: 971.52
Type: journal article
Language: en
Open access: gold
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.

Reinforcement Learning: An Introduction

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Metadata

Record

About these checks

Data sources