The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Batch Reinforcement Learning
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Citations
Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
A direct adaptive method for faster backpropagation learning: the RPROP algorithm
Riedmiller, M., & Braun, H. (2002) · IEEE International Conference on Neural Networks · doi:10.1109/icnn.1993.298623 · Cited by 3,917
Self-citationSelf-improving reactive agents based on reinforcement learning, planning and teaching
Lin, L.-J. (1992) · Machine Learning · doi:10.1007/bf00992699 · Cited by 1,564
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
Cited by
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
Haghighat, E., Raissi, M., Moure, A., Gomez, H., & Juanes, R. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.113741 · Cited by 1,305
Toward Causal Representation Learning
Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3058954 · Cited by 1,170
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Levine, S., Kumar, A., Tucker, G., & Fu, J. (2020) · arXiv · doi:10.48550/arxiv.2005.01643 · Cited by 824
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey
Azari, M. M., Solanki, S., Chatzinotas, S., Kodheli, O., Sallouha, H., Colpaert, A., Mendoza Montoya, J. F., Pollin, S., Haqiqatnejad, A., Mostaani, A., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3199901 · Cited by 668
Metadata
- Authors
- Lange, S., Gabel, T., & Riedmiller, M.
- Venue
- Adaptation, Learning, and Optimization
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 45-73
- ISSN
- 1867-4534 (ISSN-L)
- 1867-4542
- OpenAlex
- W192920577
- Cited by
- 370
- References
- 5
- FWCI
- 12.09
- Type
- book chapter
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Batch Reinforcement Learning. https://app.cochat.ai/papers/10.1007/978-3-642-27645-3_2
- The paper
- Lange, S., Gabel, T., & Riedmiller, M. (2012). Batch Reinforcement Learning. Adaptation, Learning, and Optimization. https://doi.org/10.1007/978-3-642-27645-3_2
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.
