The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Cooperative Multi-agent Control Using Deep Reinforcement Learning
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Citations
Cites
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
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Consensus and Cooperation in Networked Multi-Agent Systems
Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2006.887293 · Cited by 10,522 · cited 1×
“Multi-agent control has also been studied in extensive detail from the dynamical systems perspective in problems like formation control [10], coverage control [9], and consensus [27].”
BackgroundAsynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1×
“This idea is known as curriculum learning [4], and it can be extended to reinforcement learning problems with multiple cooperating agents.”
BackgroundCoverage Control for Mobile Sensing Networks
Cortes, J., Martinez, S., Karatas, T., & Bullo, F. (2004) · IEEE Transactions on Robotics and Automation · doi:10.1109/tra.2004.824698 · Cited by 2,710
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Zhong, M., Goddard, N., & Sutton, C. A. (2014) · Cited by 2,440
Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents
Tan, M. (1993) · Machine Learning Proceedings 1993 · doi:10.1016/b978-1-55860-307-3.50049-6 · Cited by 1,810
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
Ng, A. Y., Harada, D., & Russell, S. (1999) · Cited by 1,744
End-to-End Training of Deep Visuomotor Policies
Levine, S., Finn, C., Darrell, T. J., & Abbeel, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.00702 · Cited by 1,344
Cited by
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., & Mordatch, I. (2017) · arXiv · doi:10.48550/arxiv.1706.02275 · Cited by 3,377
Counterfactual Multi-Agent Policy Gradients
Foerster, J., Farquhar, G., Afouras, T., Nardelli, N., & Whiteson, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11794 · Cited by 1,873
Nguyen, T. T., Nguyen, N. D., & Nahavandi, S. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2977374 · Cited by 1,306
Multi-agent deep reinforcement learning: a survey
Gronauer, S., & Diepold, K. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-09996-w · Cited by 957
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
Nasir, Y. S., & Guo, D. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933973 · Cited by 660
Metadata
- Authors
- Gupta, J. K., Egorov, M., & Kochenderfer, M.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 66-83
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2768629321
- Cited by
- 874
- References
- 38
- FWCI
- 30.23
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Cooperative Multi-agent Control Using Deep Reinforcement Learning. https://app.cochat.ai/papers/10.1007/978-3-319-71682-4_5
- The paper
- Gupta, J. K., Egorov, M., & Kochenderfer, M. (2017). Cooperative Multi-agent Control Using Deep Reinforcement Learning. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-71682-4_5
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.
