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Grandmaster level in StarCraft II using multi-agent reinforcement learning

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Cited by:

  1. Jin, Jiahuan & Cui, Tianxiang & Bai, Ruibin & Qu, Rong, 2024. "Container port truck dispatching optimization using Real2Sim based deep reinforcement learning," European Journal of Operational Research, Elsevier, vol. 315(1), pages 161-175.
  2. Wang, Yong & Wu, Yuankai & Tang, Yingjuan & Li, Qin & He, Hongwen, 2023. "Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning," Applied Energy, Elsevier, vol. 332(C).
  3. 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.
  4. Michael Curry & Alexander Trott & Soham Phade & Yu Bai & Stephan Zheng, 2022. "Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning," Papers 2201.01163, arXiv.org, revised Feb 2022.
  5. Yuling Huang & Xiaoping Lu & Chujin Zhou & Yunlin Song, 2023. "DADE-DQN: Dual Action and Dual Environment Deep Q-Network for Enhancing Stock Trading Strategy," Mathematics, MDPI, vol. 11(17), pages 1-27, August.
  6. Nan Ma & Hongqi Li & Hualin Liu, 2024. "State-Space Compression for Efficient Policy Learning in Crude Oil Scheduling," Mathematics, MDPI, vol. 12(3), pages 1-16, January.
  7. Raeid Saqur, 2024. "What Teaches Robots to Walk, Teaches Them to Trade too -- Regime Adaptive Execution using Informed Data and LLMs," Papers 2406.15508, arXiv.org.
  8. Yi, Zonggen & Luo, Yusheng & Westover, Tyler & Katikaneni, Sravya & Ponkiya, Binaka & Sah, Suba & Mahmud, Sadab & Raker, David & Javaid, Ahmad & Heben, Michael J. & Khanna, Raghav, 2022. "Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system," Applied Energy, Elsevier, vol. 328(C).
  9. Thomas Dalgaty & Filippo Moro & Yiğit Demirağ & Alessio Pra & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  10. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
  11. Charbonnier, Flora & Morstyn, Thomas & McCulloch, Malcolm D., 2022. "Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility," Applied Energy, Elsevier, vol. 314(C).
  12. Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klöckl, 2024. "Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 529-576, February.
  13. Liu, Bokai & Wang, Yizheng & Rabczuk, Timon & Olofsson, Thomas & Lu, Weizhuo, 2024. "Multi-scale modeling in thermal conductivity of Polyurethane incorporated with Phase Change Materials using Physics-Informed Neural Networks," Renewable Energy, Elsevier, vol. 220(C).
  14. Rodrick Wallace, 2022. "How AI founders on adversarial landscapes of fog and friction," The Journal of Defense Modeling and Simulation, , vol. 19(3), pages 519-538, July.
  15. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
  16. Geng, Yini & Liu, Yifan & Lu, Yikang & Shen, Chen & Shi, Lei, 2022. "Reinforcement learning explains various conditional cooperation," Applied Mathematics and Computation, Elsevier, vol. 427(C).
  17. Cui, Tianxiang & Du, Nanjiang & Yang, Xiaoying & Ding, Shusheng, 2024. "Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
  18. János Kramár & Tom Eccles & Ian Gemp & Andrea Tacchetti & Kevin R. McKee & Mateusz Malinowski & Thore Graepel & Yoram Bachrach, 2022. "Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  19. Jinming Xu & Yuan Lin, 2024. "Energy Management for Hybrid Electric Vehicles Using Safe Hybrid-Action Reinforcement Learning," Mathematics, MDPI, vol. 12(5), pages 1-20, February.
  20. Wang, Xin & Liu, Shuo & Yu, Yifan & Yue, Shengzhi & Liu, Ying & Zhang, Fumin & Lin, Yuanshan, 2023. "Modeling collective motion for fish schooling via multi-agent reinforcement learning," Ecological Modelling, Elsevier, vol. 477(C).
  21. Liying Xu & Jiadi Zhu & Bing Chen & Zhen Yang & Keqin Liu & Bingjie Dang & Teng Zhang & Yuchao Yang & Ru Huang, 2022. "A distributed nanocluster based multi-agent evolutionary network," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  22. Daphne Cornelisse & Thomas Rood & Mateusz Malinowski & Yoram Bachrach & Tal Kachman, 2022. "Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members," Papers 2208.08798, arXiv.org.
  23. Dong Liu & Feng Xiao & Jian Luo & Fan Yang, 2023. "Deep Reinforcement Learning-Based Holding Control for Bus Bunching under Stochastic Travel Time and Demand," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
  24. Constantin Waubert de Puiseau & Richard Meyes & Tobias Meisen, 2022. "On reliability of reinforcement learning based production scheduling systems: a comparative survey," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 911-927, April.
  25. Weifan Long & Taixian Hou & Xiaoyi Wei & Shichao Yan & Peng Zhai & Lihua Zhang, 2023. "A Survey on Population-Based Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
  26. Xuan-Kun Li & Jian-Xu Ma & Xiang-Yu Li & Jun-Jie Hu & Chuan-Yang Ding & Feng-Kai Han & Xiao-Min Guo & Xi Tan & Xian-Min Jin, 2024. "High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
  27. Boian Lazov, 2023. "A Deep Reinforcement Learning Trader without Offline Training," Papers 2303.00356, arXiv.org.
  28. Weichao Hu & Hongzhang Mu & Yanyan Chen & Yixin Liu & Xiaosong Li, 2023. "Modeling Interactions of Autonomous/Manual Vehicles and Pedestrians with a Multi-Agent Deep Deterministic Policy Gradient," Sustainability, MDPI, vol. 15(7), pages 1-14, April.
  29. Wang, Xianjia & Yang, Zhipeng & Liu, Yanli & Chen, Guici, 2023. "A reinforcement learning-based strategy updating model for the cooperative evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
  30. Weisheng Chiu & Thomas Chun Man Fan & Sang-Back Nam & Ping-Hung Sun, 2021. "Knowledge Mapping and Sustainable Development of eSports Research: A Bibliometric and Visualized Analysis," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
  31. Weichao Mao & Tamer Başar, 2023. "Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games," Dynamic Games and Applications, Springer, vol. 13(1), pages 165-186, March.
  32. Nweye, Kingsley & Sankaranarayanan, Siva & Nagy, Zoltan, 2023. "MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities," Applied Energy, Elsevier, vol. 346(C).
  33. Malte Reinschmidt & József Fortágh & Andreas Günther & Valentin V. Volchkov, 2024. "Reinforcement learning in cold atom experiments," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  34. Bossert, Leonie & Hagendorff, Thilo, 2021. "Animals and AI. The role of animals in AI research and application – An overview and ethical evaluation," Technology in Society, Elsevier, vol. 67(C).
  35. Avishkar Bhoopchand & Bethanie Brownfield & Adrian Collister & Agustin Dal Lago & Ashley Edwards & Richard Everett & Alexandre Fréchette & Yanko Gitahy Oliveira & Edward Hughes & Kory W. Mathewson & P, 2023. "Learning few-shot imitation as cultural transmission," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  36. Yang, Zhengzhi & Zheng, Lei & Perc, Matjaž & Li, Yumeng, 2024. "Interaction state Q-learning promotes cooperation in the spatial prisoner's dilemma game," Applied Mathematics and Computation, Elsevier, vol. 463(C).
  37. Mohit Sewak & Sanjay K. Sahay & Hemant Rathore, 2023. "Deep Reinforcement Learning in the Advanced Cybersecurity Threat Detection and Protection," Information Systems Frontiers, Springer, vol. 25(2), pages 589-611, April.
  38. Qian Zhang & Tianhao Li & Dengfeng Li & Wei Lu, 2024. "A goal-oriented reinforcement learning for optimal drug dosage control," Annals of Operations Research, Springer, vol. 338(2), pages 1403-1423, July.
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