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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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Jiaming Li, Ning Xie and Tingting Zhao
In recent years, with the rapid advancements in Natural Language Processing (NLP) technologies, large models have become widespread. Traditional reinforcement learning algorithms have also started experimenting with language models to optimize training. ...
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Ziyi Wang, Xinran Li, Luoyang Sun, Haifeng Zhang, Hualin Liu and Jun Wang
Efficient yet sufficient exploration remains a critical challenge in reinforcement learning (RL), especially for Markov Decision Processes (MDPs) with vast action spaces. Previous approaches have commonly involved projecting the original action space int...
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Junlin Lou, Burak Yuksek, Gokhan Inalhan and Antonios Tsourdos
In this study, we consider the problem of motion planning for urban air mobility applications to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal location in the presence of dynamic geo-fences and uncertainties in ...
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Shui Jiang, Yanning Ge, Xu Yang, Wencheng Yang and Hui Cui
Reinforcement learning (RL) is pivotal in empowering Unmanned Aerial Vehicles (UAVs) to navigate and make decisions efficiently and intelligently within complex and dynamic surroundings. Despite its significance, RL is hampered by inherent limitations su...
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Xu Feng, Mengyang He, Lei Zhuang, Yanrui Song and Rumeng Peng
SAGIN is formed by the fusion of ground networks and aircraft networks. It breaks through the limitation of communication, which cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, many heterogeneo...
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Huiting Wang, Yazhi Liu, Wei Li and Zhigang Yang
In data center networks, when facing challenges such as traffic volatility, low resource utilization, and the difficulty of a single traffic scheduling strategy to meet demands, it is necessary to introduce intelligent traffic scheduling mechanisms to im...
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Hao Sun, Zile Jia, Meng Zhao, Jiayuan Tian, Dan Liu and Yifei Wang
The current lack of a high-precision, real-time model applicable to the control optimization process of heat exchange systems, especially the difficulty in determining the overall heat transfer coefficient K of heat exchanger operating parameters in real...
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Jinhui Guo, Xiaoli Zhang, Kun Liang and Guoqiang Zhang
In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shifted....
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Sungwon Moon, Seolwon Koo, Yujin Lim and Hyunjin Joo
With recent technological advancements, the commercialization of autonomous vehicles (AVs) is expected to be realized soon. However, it is anticipated that a mixed traffic of AVs and human-driven vehicles (HVs) will persist for a considerable period unti...
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