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Reinforcement Learning: An Introduction
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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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Metadata
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2005-01
- Volume
- 16
- Issue
- 1
- Pages
- 285-286
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2121863487
- Cited by
- 24,496
- References
- 66
- FWCI
- 971.52
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
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