The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Comprehensive end-to-end workflow orchestration”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [70]
In the citing paper
“Multi-agent systems use less energy in smart buildings while maintaining comfort, reducing cost, and preventing energy spikes [70].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [70]
In the citing paper
“Sensor data can detect a building's power grid [73], analyze how it is being used, and instruct the building to save energy [70].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [70]
In the citing paper
“Cooperation behavior is actively promoted using the MAAC model [70] to improve resource management across multiple agents.”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [70]
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
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
Reinforcement Learning: An Introduction
Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,825
Soft actor-critic Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018) · arXiv · doi:10.48550/arxiv.1801.01290 · Cited by 5,594
(1999) · Cited by 5,147
Deep Reinforcement Learning: A Brief Survey
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733
Addressing Function Approximation Error in Actor-Critic Methods
Fujimoto, S., van Hoof, H., & Meger, D. (2018) · arXiv · doi:10.48550/arxiv.1802.09477 · Cited by 3,650
EnergyPlus: creating a new-generation building energy simulation program
Crawley, D. B., Lawrie, L. K., Winkelmann, F. C., Buhl, W. F., Huang, Y. J., PEDERSEN, C. O., Strand, R. K., Liesen, R. J., Fisher, D. E., Witte, M. J., et al. (2001) · Energy and Buildings · doi:10.1016/s0378-7788(00)00114-6 · Cited by 2,853
Konda, V. R., & Tsitsiklis, J. N. (2002) · Cited by 2,202
Research works printed but not linked: 2
- “An assessment of energy technologies and research opportunities,” Quadrennial Technology Review. United States Department of Energy, pp. 12-19, 2015.
- Littman, 1994, “Markov games as a framework for multi-agent reinforcement learning,” in Machine learning proceedings, Elsevier, 1994, 157
Cited by
Sorted by citations
Items 101–109 of 109
MA-ML: Multiagents and Multilayer Collaboration Architectures
Nof, S. Y. (2026) · Automation, collaboration, and e-services · doi:10.1007/978-3-032-14922-0_3
Kumar, D., Rana, S. K., Verma, S., Kumar, D., & Singh, P. (2026) · International Conference on Electrical and Electronics for Sustainable Innovations (ICEESI) · doi:10.1109/iceesi68020.2026.11583310
Simoiu, M. Ş., Ploix, S., Boulmaiz, F., & Phouratsamay, S.-L. (2026) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2026.102129
He, J., Yu, L., Qiu, D., Zhang, M., Ye, Y., Wang, Y., Štrbac, G., & Yue, D. (2026) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2026.3715858
Peak-Shaving Ancillary Service Demand Power Optimization Based on SAC Algorithm
Yao, S., Cheng, X., Wang, H. T., Chen, Z. Y., Chen, W., Li, J., & Li, Y. (2026) · International Conference on Energy Systems and Electrical Power (ICESEP) · doi:10.1109/icesep70386.2026.11608033
Gupta, A., & Harasek, M. (2026) · Sustainable Chemistry · doi:10.3390/suschem7030040
Chen, C., & Huang, X. (2026) · International Conference on Information Communication, IoT Technology, and Smart Cities (IITS) · doi:10.1109/iits69561.2026.11648460
Mohammadi, B., Ranjbar, M. A., Baghaee, H. R., & Haghifam, M. R. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124247
Kapparova, A., Galland, S., Lombard, A., Nurgaliyev, M., Dosymbetova, G., Kaniyev, Y., Saymbetov, A., Bolatbek, A., Orynbassar, S., & Жоламанов, Б. (2026) · Smart Cities · doi:10.3390/smartcities9100177
Metadata
- Authors
- Xie, J., Ajagekar, A., & You, F.
- Affiliations
- Cornell University (US)
- Corresponding author
- You, Fengqi
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2023-07
- Volume
- 342
- Pages
- 121162
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W4367046676
- Cited by
- 109
- References
- 51
- Citations by year
- 2026: 32 · 2025: 40 · 2024: 29 · 2023: 8
- FWCI
- 7.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Applied Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Keywords
- demand response, multi-agent deep reinforcement learning, actor-critic, building energy management, load coordination, electricity cost reduction, load shaping, renewable energy integration, demand-side flexibility
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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.
Statistics and arithmetic are recomputed, not judged.
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