The DOI matches its Crossref record; 258 of the 258 references it cites were found in a registry or an index.
Reinforcement learning algorithms: A brief survey
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Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
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
Cited by
Multi-Agent Deep Reinforcement Learning for Scheduling of Energy Storage System in Microgrids
Jung, S.-W., An, Y.-Y., Suh, B., Park, Y., Kim, J., & Kim, K.-I. (2025) · Mathematics · doi:10.3390/math13121999 · Cited by 28
Zhao, R., Tao, S., & Li, P. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2024.102942 · Cited by 22
Guo, J., Zhou, Y., Burlion, L., Savkin, A. V., & Huang, C. (2025) · Control Engineering Practice · doi:10.1016/j.conengprac.2025.106491 · Cited by 13
Sensorimotor Mechanisms of Decisions and Actions
James, T. W. (2026) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.a.2484
Metadata
- Authors
- Shakya, A. K., Pillai, G., & Chakrabarty, S.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2023-11
- Volume
- 231
- Pages
- 120495
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W4377695283
- Cited by
- 656
- References
- 258
- FWCI
- 57.63
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Reinforcement learning algorithms: A brief survey. https://app.cochat.ai/papers/10.1016/j.eswa.2023.120495
- The paper
- Shakya, A. K., Pillai, G., & Chakrabarty, S. (2023). Reinforcement learning algorithms: A brief survey. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2023.120495
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.
