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Juyao Wei, Zhenggang Lu, Zheng Yin and Zhipeng Jing
This paper presents a novel data-driven multiagent reinforcement learning (MARL) controller for enhancing the running stability of independently rotating wheels (IRW) and reducing wheel?rail wear. We base our active guidance controller on the multiagent ...
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Yu Dai, Jiaming Fu, Zhen Gao and Lei Yang
Due to CPU and memory limitations, mobile IoT devices face challenges in handling delay-sensitive and computationally intensive tasks. Mobile edge computing addresses this issue by offloading tasks to the wireless network edge, reducing latency and energ...
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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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Sheng Yu, Wei Zhu and Yong Wang
Wargames are essential simulators for various war scenarios. However, the increasing pace of warfare has rendered traditional wargame decision-making methods inadequate. To address this challenge, wargame-assisted decision-making methods that leverage ar...
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