The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Recent Advances in Hierarchical Reinforcement Learning
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Citations
Cites
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119
Toward a modern theory of adaptive networks: Expectation and prediction.
Sutton, R. S., & Barto, A. G. (1981) · Psychological Review · doi:10.1037/0033-295x.88.2.135 · Cited by 1,450
Self-citationA Unified Framework for Hybrid Control: Model and Optimal Control Theory
Branicky, M. S., Borkar, V. S., & Mitter, S. K. (1998) · IEEE Transactions on Automatic Control · doi:10.1109/9.654885 · Cited by 1,313
The Complexity of Decentralized Control of Markov Decision Processes
Bernstein, D. S., Givan, R., Immerman, N., & Zilberstein, S. (2002) · Mathematics of Operations Research · doi:10.1287/moor.27.4.819.297 · Cited by 1,192
A model for reasoning about persistence and causation
Dean, T., & Kanazawa, K. (1989) · Computational Intelligence · doi:10.1111/j.1467-8640.1989.tb00324.x · Cited by 999
The hierarchical hidden Markov model: Analysis and applications
Fine, S., Singer, Y., & Tishby, N. (1998) · Machine Learning · doi:10.1023/a:1007469218079 · Cited by 906
Cited by
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
Deep reinforcement learning for autonomous driving: A survey
Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A. A. A., Yogamani, S., & Perez, P. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3054625 · Cited by 2,342
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Kulkarni, T. D., Narasimhan, K., Saeedi, A., & Tenenbaum, J. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.06057 · Cited by 770
Deep Reinforcement Learning: An Overview
Li, Y. (2017) · arXiv · doi:10.48550/arxiv.1701.07274 · Cited by 762
Metadata
- Authors
- Barto, A. G., & Mahadevan, S.
- Venue
- Discrete Event Dynamic Systems
- Published
- 2003-01
- Volume
- 13
- Issue
- 1-2
- Pages
- 41-77
- ISSN
- 0924-6703 (ISSN-L)
- 1573-7594
- OpenAlex
- W1592847719
- Cited by
- 1,024
- References
- 8
- FWCI
- 34.28
- Type
- journal article
- Language
- en
- Open access
- closed
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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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- Barto, A. G., & Mahadevan, S. (2003). Recent Advances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems. https://doi.org/10.1023/a%3A1022140919877
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.
