Multiagent cooperation and competition with deep reinforcement learning
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DOI: 10.1371/journal.pone.0172395
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Cited by:
- Qingyan Li & Tao Lin & Qianyi Yu & Hui Du & Jun Li & Xiyue Fu, 2023. "Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control," Energies, MDPI, vol. 16(10), pages 1-23, May.
- Li, Xingyu & Epureanu, Bogdan I., 2020. "AI-based competition of autonomous vehicle fleets with application to fleet modularity," European Journal of Operational Research, Elsevier, vol. 287(3), pages 856-874.
- Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 318(C).
- Se-Heon Lim & Sung-Guk Yoon, 2022. "Dynamic DNR and Solar PV Smart Inverter Control Scheme Using Heterogeneous Multi-Agent Deep Reinforcement Learning," Energies, MDPI, vol. 15(23), pages 1-18, December.
- Wang, Xuekai & D’Ariano, Andrea & Su, Shuai & Tang, Tao, 2023. "Cooperative train control during the power supply shortage in metro system: A multi-agent reinforcement learning approach," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 244-278.
- Tianhao Wang & Shiqian Ma & Na Xu & Tianchun Xiang & Xiaoyun Han & Chaoxu Mu & Yao Jin, 2022. "Secondary Voltage Collaborative Control of Distributed Energy System via Multi-Agent Reinforcement Learning," Energies, MDPI, vol. 15(19), pages 1-12, September.
- Lee, Hyun-Rok & Lee, Taesik, 2021. "Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response," European Journal of Operational Research, Elsevier, vol. 291(1), pages 296-308.
- Aymanns, Christoph & Foerster, Jakob & Georg, Co-Pierre & Weber, Matthias, 2022.
"Fake News in Social Networks,"
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y4mkd, Center for Open Science.
- Christoph Aymanns & Jakob Foerster & Co-Pierre Georg & Matthias Weber, 2022. "Fake News in Social Networks," Swiss Finance Institute Research Paper Series 22-58, Swiss Finance Institute.
- Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2019. "Algorithmic Pricing What Implications for Competition Policy?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 55(1), pages 155-171, August.
- Young Joon Park & Yoon Sang Cho & Seoung Bum Kim, 2019. "Multi-agent reinforcement learning with approximate model learning for competitive games," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-20, September.
- Marilleau, Nicolas & Lang, Christophe & Giraudoux, Patrick, 2018. "Coupling agent-based with equation-based models to study spatially explicit megapopulation dynamics," Ecological Modelling, Elsevier, vol. 384(C), pages 34-42.
- Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
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