Showing 1 - 20 results of 25 for search '"duel"', query time: 0.08s Refine Results
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    Dueling Network Architecture for GNN in the Deep Reinforcement Learning for the Automated ICT System Design by Tianchen Zhou, Yutaka Yakuwa, Natsuki Okamura, Hiroyuki Hochigai, Takayuki Kuroda, Ikuko Eguchi Yairi

    Published 2025-01-01
    “…The proposed method aims to improve end-to-end deep Q learning with a GNN by decomposing the GNN-based Q-network structure into two sub-streams to separately estimate the global state value and the state-dependent action advantage instead. By doing that, our dueling GNN architecture can independently learn which states are valuable or not. …”
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    Dynamics of special endurance of athletes aged 13-15 years under the influence of the program of the Cossack duel by Andrij Plokhikh, Gennadij Ogar

    Published 2022-12-01
    “…The purpose of the study is to identify the impact of the Cossack duel program on the special endurance of athletes aged 13-15 year. …”
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    DUAL EDUCATIONAL SYSTEM AS A FOCILITATOR OF SUCCESSFUL PROFESSIONAL AND SOCIAL ADAPTATION OF FUTURE SPECIALISTS by Y. A. Yugfeld, N. V. Pankina

    Published 2015-03-01
    “…In conclusion, the paper discusses the experience of Pervouralsk Metallurgical College implementing the duel system aimed at vocational training of qualified and competitive specialists.…”
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    Traffic Flow Characteristics and Lane Use Strategies for Connected and Automated Vehicles in Mixed Traffic Conditions by Zijia Zhong, Joyoung Lee, Liuhui Zhao

    Published 2021-01-01
    “…As the result shows, the highest flow rate is 3400 vehicles per hour per lane at 90% market penetration rate with one CAV lane. (2) The concentration of CAVs in one lane results in a narrower headway distribution (with smaller standard deviation) even with partial market penetration. (3) A dedicated CAV lane is also able to eliminate duel-bell-shape distribution that is caused by the heterogeneous traffic flow. (4) A dedicated CAV lane creates a more consistent CAV density, which facilitates communication activity and decreases the probability of packet dropping.…”
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    The Soviet-British Relations at the Turn of 1917/1918: Key Issues and Untapped Opportunities by E. Yu. Sergeev

    Published 2020-11-01
    “…The author concludes that throughout this period neither side managed to win the diplomatic duel: Great Britain had lost its strategic ally whereas Russia after four years of struggle had to repel the military intervention of its ex-partners in the Entente.…”
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    Recent advances in the role of high-salt diet in anti- and pro-cancer progression by Shiwei Tang, Juan Xu, Ping Wan, Ping Wan, Ping Wan, Shumen Jin, Ying Zhang, Ying Zhang, Linting Xun, Linting Xun, Jinli Wang, Jinli Wang, Mei Luo, Mei Luo, Wenjie Chen, Wenjie Chen, Zan Zuo, Zan Zuo, Zan Zuo, Hui Tang, Hui Tang, Jialong Qi, Jialong Qi, Jialong Qi, Jialong Qi, Jialong Qi

    Published 2025-01-01
    “…This review focused on the duel molecular mechanisms of HSD in cancer development, which are based on the tumor microenvironment, the gut microbiota, and the involvement of sodium transporter channels. …”
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    Intelligent Demand Response Resource Trading Using Deep Reinforcement Learning by Yufan Zhang, Qian Ai, Zhaoyu Li

    Published 2024-01-01
    “…To protect their privacy, a dueling deep Q-network (dueling DQN) is then constructed to model the bi-Ievel Stackelberg game, such that the lower-level problem doesn't need to reveal its detailed model to the upper-level. …”
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    SGD-TripleQNet: An Integrated Deep Reinforcement Learning Model for Vehicle Lane-Change Decision by Yang Liu, Tianxing Yang, Liwei Tian, Jianbiao Pei

    Published 2025-01-01
    “…This method integrates three types of deep Q-learning networks (DQN, DDQN, and Dueling DDQN) and uses the Stochastic Gradient Descent (SGD) optimization algorithm to dynamically adjust the network weights. …”
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    Resource allocation strategy based on deep reinforcement learning in 6G dense network by Fan YANG, Cheng YANG, Jie HUANG, Shilong ZHANG, Tao YU, Xun ZUO, Chuan YANG

    Published 2023-08-01
    “…In order to realize no overlapping interference between cells, 6G dense network (DN) adopting resource allocation is the important technology of enhancing network performance.However, limited resources and dense distribution of nodes make it difficult to solve the problem of resource allocation through traditional optimization methods.To tackle the problem, a point-line graph coloring based overlapping interference model was formulated and a Dueling deep Q-network (DQN) based resource allocation method was proposed, which combined deep reinforcement learning (DRL) and the overlapping interference model.Specifically, the proposed method adopted the overlapping interference model and resource reuse rate to design the immediate reward.Then, generating 6G DN resource allocation strategies were independently learned by using Dueling DQN to achieve the goal of realizing resource allocation without overlapping interference between cells.The performance evaluation results show that the proposed method can effectively increase both network throughput and resource reuse rate, as well as enhance network performance.…”
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    Energy Management in Microgrids Using Model-Free Deep Reinforcement Learning Approach by Odia A. Talab, Isa Avci

    Published 2025-01-01
    “…The results show a total cost of 51.8770 €ct/kWh, representing a reduction of 3.19% compared to the Dueling Deep Q Network (Dueling DQN) and 4% compared to the Deep Q Network (DQN). …”
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    Carolina Andrade, A orillas de un relato by Cecilia Vera de Gálvez

    Published 2025-01-01
    “… Del cero al diez, ¿cuánto le duele? ... Entre esas dos cifras, entre entregarse a la tragedia o al cinismo, cualquier número cabe, siempre y cuando se sustente en una historia verosímil. …”
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    Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice by Guolin SUN, Ruijie OU, Guisong LIU

    Published 2020-09-01
    “…Based on the requirements of ultra-low latency services for emergency Internet-of-things (EIoT) applications,a multi-slice network architecture for ultra-low latency emergency IoT was designed,and a general methodology framework based on resource reservation,sharing and isolation for multiple slices was proposed.In the proposed framework,real-time and automatic inter-slice resource demand prediction and allocation were realized based on deep reinforcement learning (DRL),while intra-slice user resource allocation was modeled as a shape-based 2-dimension packing problem and solved with a heuristic numerical algorithm,so that intra-slice resource customization was achieved.Simulation results show that the resource reservation-based method enable EIoT slices to explicitly reserve resources,provide a better security isolation level,and DRL could guarantee accuracy and real-time updates of resource reservations.Compared with four existing algorithms,dueling deep Q-network (DQN) performes better than the benchmarks.…”
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    Public relations ethics : the real-world guide / by Morris, Trevor, Goldsworthy, Simon

    Published 2021
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    Secure transmission scheme for millimeter-wave Internet of vehicles with multiple base stations and users by JU Ying, CHEN Yuchao, TIAN Suheng, LIU Lei, LI Zan, PEI Qingqi, WANG Mingyang

    Published 2024-08-01
    “…For the timeliness of millimeter-wave secure beamforming in mobile scenarios and the robustness of secure connections in millimeter-wave dynamic eavesdropping scenarios, a multi-agent secure cooperative communication scheme based on a dueling double deep Q network (D3QN)-deep deterministic policy gradient (DDPG) algorithm was proposed to address communication security issues. …”
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