The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Multi-agent Reinforcement Learning: An Overview
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
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
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
A Comprehensive Survey of Multiagent Reinforcement Learning
Busoniu, L., Babuska, R., & De Schutter, B. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.913919 · Cited by 2,235
Cooperative Multi-Agent Learning: The State of the Art
Panait, L., & Luke, S. (2005) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-005-2631-2 · Cited by 1,237
Multiagent systems: A survey from a machine learning perspective
Stone, P., & Veloso, M. (2000) · Autonomous Robots · doi:10.1023/a:1008942012299 · Cited by 1,218
Team-partitioned, opaque-transition reinforcement learning
Stone, P., & Veloso, M. (1999) · Proceedings of the third annual conference on Autonomous Agents · doi:10.1145/301136.301195 · Cited by 114
Cited by
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
Deep Reinforcement Learning: A Survey
Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., Dai, B., & Miao, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3207346 · Cited by 972
Robust adversarial reinforcement learning
Pinto, L., Davidson, J., Sukthankar, R., & Gupta, A. (2017) · arXiv · doi:10.48550/arxiv.1703.02702 · Cited by 507
A review of cooperative multi-agent deep reinforcement learning
Oroojlooy, A., & Hajinezhad, D. (2023) · Applied Intelligence · doi:10.1007/s10489-022-04105-y · Cited by 482
Application of deep reinforcement learning to intrusion detection for supervised problems
Lopez-Martin, M., Carro, B., & Sanchez-Esguevillas, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112963 · Cited by 360
Metadata
- Authors
- Buşoniu, L., Babuška, R., & De Schutter, B.
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer Berlin Heidelberg
- Published
- 2010
- Pages
- 183-221
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W206679605
- Cited by
- 741
- References
- 9
- FWCI
- 7.85
- Type
- book chapter
- Language
- en
- Open access
- closed
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Multi-agent Reinforcement Learning: An Overview. https://app.cochat.ai/papers/10.1007/978-3-642-14435-6_7
- The paper
- Buşoniu, L., Babuška, R., & De Schutter, B. (2010). Multi-agent Reinforcement Learning: An Overview. Studies in Computational Intelligence. https://doi.org/10.1007/978-3-642-14435-6_7
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.
