The DOI matches its Crossref record.
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
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Cited by
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Ingrand, F., & Ghallab, M. (2017) · Artificial Intelligence · doi:10.1016/j.artint.2014.11.003 · Cited by 309
Internally generated sequences in learning and executing goal-directed behavior
Pezzulo, G., van der Meer, M. A. A., Lansink, C. S., & Pennartz, C. M. A. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.06.011 · Cited by 217
An object-oriented representation for efficient reinforcement learning
Diuk, C., Cohen, A., & Littman, M. L. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390187 · Cited by 226
The algorithmic anatomy of model-based evaluation
Daw, N. D., & Dayan, P. (2014) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2013.0478 · Cited by 195
DESPOT: Online POMDP Planning with Regularization
Ye, N., Somani, A., Hsu, D., & Lee, W. S. (2017) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5328 · Cited by 179
Reinforcement Learning with Perturbed Rewards
Wang, J., Liu, Y., & Li, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6086 · Cited by 111
PAC Reinforcement Learning with Rich Observations
Krishnamurthy, A., Agarwal, A., & Langford, J. (2016) · arXiv · doi:10.48550/arxiv.1602.02722 · Cited by 13
Efficient PAC Reinforcement Learning in Regular Decision Processes
Ronca, A., & De Giacomo, G. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/279 · Cited by 6
On Value Functions and the Agent-Environment Boundary
Jiang, N. (2019) · arXiv · doi:10.48550/arxiv.1905.13341 · Cited by 5
Metadata
- Authors
- Kearns, M., Mansour, Y., & Ng, A. Y.
- Venue
- Machine Learning
- Published
- 2002-11
- Volume
- 49
- Issue
- 2-3
- Pages
- 193-208
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1512919909
- Cited by
- 488
- References
- 0
- FWCI
- 13.05
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
Statistics and arithmetic are recomputed, not judged.
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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.