Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning by Qingli Liu, Yongjie Ma

“…A resource allocation method based on federated multi-agent deep reinforcement learning is proposed for Vehicular Networking communication, by fusing Asynchronous Federated Learning (AFL) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). …”
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Pharmacoeconomic study of fluorescent lymphography and radionuclide diagnostics methods for sentinel lymph node detection in breast cancer by E. P. Kulikov, M. V. Shomova, D. S. Titov, A. N. Demko, M. A. Maistrenko

“…Sentinel lymph node (SLN) biopsy is a reliable diagnostic method used to assess the spread of the malignant process in regional lymph nodes. …”
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Marine Voyage Optimization and Weather Routing with Deep Reinforcement Learning by Charilaos Latinopoulos, Efstathios Zavvos, Dimitrios Kaklis, Veerle Leemen, Aristides Halatsis

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A data-physical fusion method for economic dispatch considering high renewable penetration and security constraints by Yuchen Dai, Wei Xu, Xiaokang Wu, Minghui Yan, Feng Xue, Jianfeng Zhao

“…Conventional model-based methods of economic dispatch encounter significant challenges due to the increasing uncertainties brought about by high renewable penetration. …”
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Intra-day dispatch method via deep reinforcement learning based on pre-training and expert knowledge by Yanbo Chen, Qintao Du, Huayu Dong, Tao Huang, Jiahao Ma, Zitao Xu, Zhihao Wang

“…In recent years, due to high self-learning and self-optimization ability, reinforcement learning has emerged in the field of economic dispatch, which can solve model-free dynamic programming problems that cannot be effectively solved by traditional optimization methods. In this paper, we construct a reinforcement agent for intra-day dispatch to optimize generator output, using a twin delayed deep deterministic policy gradient algorithm based on pre-training and expert knowledge (PEK-TD3). …”
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The Constraint Function Response Shifting Scalar-Based Optimization Method for the Reliability-Based Dynamic Optimization Problem by Ping Qiao, Qi Zhang, Yizhong Wu

“…Whereafter, in order to solve RB-DOP efficiently, the constraint function response shift scalar (CFRSS)-based RB-DOP optimization method is proposed, in which the nested RB-DOP is decoupled into an equivalent deterministic DOP and a CFRSS search problem, and the two problems are addressed iteratively until the control law converges. …”
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A Practical Cache Partitioning Method for Multi-Core Processor on a Commercial Safety-Critical Partitioned RTOS by Taeho Kim

“…Validation in a flight simulation environment confirmed the practicality of the method in real-world scenarios.…”
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High-Order Spectral Method of Density Estimation for Stochastic Differential Equation Driven by Multivariate Gaussian Random Variables by Hongling Xie

“…There are some previous works on designing efficient and high-order numerical methods of density estimation for stochastic partial differential equation (SPDE) driven by multivariate Gaussian random variables. …”
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Age of Information Minimization in Vehicular Edge Computing Networks: A Mask-Assisted Hybrid PPO-Based Method by Xiaoli Qin, Zhifei Zhang, Chanyuan Meng, Rui Dong, Ke Xiong, Pingyi Fan

“…Simulation results show that the proposed MHPPO method achieves an approximately 28.9% reduction in AoI compared with the HPPO method and about a 23% reduction compared with the mask-assisted deep deterministic policy gradient (MDDPG).…”
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Reinforcement learning in electric vehicle energy management: a comprehensive open-access review of methods, challenges, and future innovations by Georginio Ananganó-Alvarado, Ignacio Umaña-Morel, Brian Keith-Norambuena

“…Key contributions include a comparative mapping of reinforcement learning techniques—such as Q-learning, deep deterministic policy gradient, twin delayed deep deterministic policy gradient and soft actor-critic—their applicability to electric vehicle control scenarios, and the identification of current research gaps and deployment challenges. …”
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Comparative Analysis of Some Methods and Algorithms for Traffic Optimization in Urban Environments Based on Maximum Flow and Deep Reinforcement Learning by Silvia Baeva, Nikolay Hinov, Plamen Nakov

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Reinforcement learning for autonomous underwater vehicles (AUVs): navigating challenges in dynamic and energy-constrained environments by Mohab M. Eweda, Karim ElNaggar

“…Nevertheless, navigation, obstacle avoidance, and energy efficiency are greatly hindered by the ever-changing underwater environments. …”
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Secure THz Communication in 6G: A Two-Stage DRL Approach for IRS-Assisted NOMA by Muhammad Shahwar, Manzoor Ahmed, Touseef Hussain, Muhammad Sheraz, Wali Ullah Khan, Teong Chee Chuah

“…Utilizing the deep deterministic policy gradient (DDPG) algorithm, we introduce a two-stage policy learning approach designed to optimize secrecy energy efficiency (SEE) while ensuring secure communication, even in the presence of multiple eavesdroppers (Eves). …”
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Numerical implementation of a stochastic differential equation of motion by Saúl Moisés Torres Murga

“…In order to extract solutions from this equation, the Euler-Maruyama method was used, which was implemented computationally. …”
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Two-sided Energy Storage Cooperative Scheduling Method for Transmission and Distribution Network Based on Multi-agent Attention-deep Reinforcement Learning by CHEN Shi, ZHU Yujie, LIU Yihong, XU Liuchao, TANG Guodeng

“…ObjectiveIn the new power system, energy storage devices are constrained by geographical limitations and single dispatch methods, leading to low utilization efficiency, which severely restricts the effective integration of renewable energy. …”
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Accelerated Computation of Linear Complementarity Problem in Dexterous Robotic Grasping via Newton-Subgradient Non-Smooth Multi-Step Greedy Kaczmarz Method by Zhiwei Ai, Chenliang Li

“…The methodology effectively mitigates inherent limitations of conventional randomized row selection, including unpredictable iteration counts and computational overhead from repeated Jacobian updates, while maintaining deterministic convergence behavior. The method’s convergence theory is rigorously established, with benchmark analyses demonstrating marked improvements in computational efficiency over the NSNGRK framework. …”
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Parameter uncertainties for imperfect surrogate models in the low-noise regime by Thomas D Swinburne, Danny Perez

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Integrated estimation of parameters of radio transmitter power amplifier with automatic mode adjustment by two-frequency test signal by P. V. Sak

“…Method is proposed for calculation of energy gain and efficiency factor (efficiency) when applying automatic control of supply voltage of output cascades of shortwave transmitters intended for modulation with speech signals. …”
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Reliability-Based Optimum Design of Dome Truss Structures through Enhanced Vibration Particle System by Pedram Hosseini, Nasibeh Hatami, Seyed Rohollah Hoseini Vaez

“…The Reliability-Based Design Optimization (RBDO) method has been utilized to create the most efficient and safe design of structures. …”
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Maximizing theoretical and practical storage capacity in single-layer feedforward neural networks by Zane Z. Chou, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller

“…This work offers a foundational framework for maximizing storage efficiency in neural network systems and supports the development of data-efficient, sustainable AI.…”
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