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    Research on Adaptive Education Path Dynamic Programming Algorithm Based on Reinforcement Learning and Cognitive Graphs by Hongli Lou, Pin Yue, Jianwen Chen

    Published 2025-01-01
    “…By employing reinforcement learning algorithms, the system continuously refines its model based on learner feedback and past interactions. …”
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    Research on Pump Down Treating Pressure Prediction Based on New Particle Swarm Optimization Algorithm by LIU Mingming, SONG Zhiyong, MA Shou, WEI Yuhua, ZHUANG Huanle

    Published 2024-06-01
    “…This paper proposes a pump down friction model based on the Bernoulli equation of interstitial flow, and establishes a mathematical model of pump down treating pressure based on drag friction and perforation friction, and further uses a new particle swarm algorithm and the least squares method to establish an automatic history matching method for pump down treating pressure. …”
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    Research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in SDCN by Shouhua JIANG, Yiwu WANG

    Published 2024-02-01
    “…With the continuous development of network technology, the network topology distributed network control mode based on Fat-Tree gradually reveals its limitations.Software-defined data center network (SDCN) technology, as an improved technology of Fat-Tree network topology, has attracted more and more researchers’ attention.Firstly, an edge computing architecture in SDCN and a task offloading model based on the three-layer service architecture of the mobile edge computing (MEC) platform were built, combined with the actual application scenarios of the MEC platform.Through the same strategy experience playback and entropy regularization, the traditional deep Q-leaning network (DQN) algorithm was improved, and the task offloading strategy of MEC platform was optimized.An improved DQN algorithm based on same strategy empirical playback and entropy regularization (RSS2E-DQN) was compared with three other algorithms in load balancing, energy consumption, delay and network usage.It is verified that the improved algorithm has better performance in the above four aspects.…”
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