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Zheng Li, Xinkai Chen, Jiaqing Fu, Ning Xie and Tingting Zhao
With the development of electronic game technology, the content of electronic games presents a larger number of units, richer unit attributes, more complex game mechanisms, and more diverse team strategies. Multi-agent deep reinforcement learning shines ...
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Yusheng Chen, Zhaofa Sun, Yanmei Wang, Ye Ma and Weili Yang
In the context of global food security and the pursuit of sustainable agricultural development, fostering synergistic innovation in the seed industry is of strategic importance. However, the collaborative innovation process between seed companies, resear...
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Bernhard Jonathan Sattler, John Friesen, Andrea Tundis and Peter F. Pelz
Current challenges, such as climate change or military conflicts, show the great importance of urban supply infrastructures. In this context, an open question is how different scenarios and crises can be studied in silico to assess the interaction betwee...
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Hao Xu, Jun Zhang, Xizhen Xu, Zewei Zeng, Yuzhu Xu, Jiawei You and Jing Li
Green residences have enormous potential for energy savings, emission reduction, and other comprehensive benefits, and their growth is crucial to achieving China?s carbon neutrality and carbon peaking targets. Nevertheless, at the moment, the national gr...
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Yihan Niu, Feixiang Zhu, Moxuan Wei, Yifan Du and Pengyu Zhai
Maritime Autonomous Surface Ships (MASS) are becoming of interest to the maritime sector and are also on the agenda of the International Maritime Organization (IMO). With the boom in global maritime traffic, the number of ships is increasing rapidly. The...
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Ali Taherizadeh and Shiva Zamani
In this study, we explore the dynamics of the stock market using an agent-based simulation platform. Our approach involves creating a multi-strategy market where each agent considers both fundamental and technical factors when determining their strategy....
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Jun Long, Shimin Wu, Xiaodong Han, Yunbo Wang and Limin Liu
The increasing number of satellites for specific space tasks makes it difficult for traditional satellite task planning that relies on ground station planning and on-board execution to fully exploit the overall effectiveness of satellites. Meanwhile, the...
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Herbert Palm and Lorin Arndt
The multi-objective optimization (MOO) of complex systems remains a challenging task in engineering domains. The methodological approach of applying MOO algorithms to simulation-enabled models has established itself as a standard. Despite increasing in c...
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Shao Xuan Seah and Sutthiphong Srigrarom
This paper explores the use of deep reinforcement learning in solving the multi-agent aircraft traffic planning (individual paths) and collision avoidance problem for a multiple UAS, such as that for a cargo drone network. Specifically, the Deep Q-Networ...
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Xiaoping Zhang, Yuanpeng Zheng, Li Wang, Arsen Abdulali and Fumiya Iida
Multi-agent collaborative target search is one of the main challenges in the multi-agent field, and deep reinforcement learning (DRL) is a good way to learn such a task. However, DRL always faces the problem of sparse reward, which to some extent reduces...
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