Decision-Making Policy for Autonomous Vehicles on Highways Using Deep Reinforcement Learning (DRL) Method by Ali Rizehvandi, Shahram Azadi, Arno Eichberger

“…Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway overtaking scenarios. …”
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FedDDPG: A reinforcement learning method for federated learning-based vehicle trajectory prediction by Jinlong Li, Ruonan Li, Guojie Ma, Weihong Yang, Hongye Wang, Zhaoquan Gu

“…However, trajectory data collected from roadside units often contains varying levels of noise, which poses unique challenges for traditional FL methods. To address these challenges, this paper proposes a personalized optimization solution called FedDDPG (Federated Learning with Deep Deterministic Policy Gradient) for VTP with FL paradigm. …”
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Quantitative operation risk assessment method for power grid with large-scale distributed new energy by Shuai ZHAO, Xiaolin ZHENG, Tao LU, Xiaojing YANG, Ning JIANG, Haipeng LIU

“…Finally, the application case results of actual power grid show that the risk assessment method proposed in this paper not only improves the risk assessment accuracy and calculation efficiency of the power grid with distributed new energy, but also can more comprehensively reflect the real-time operation risk characteristics of the system.…”
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A Distributional Robust Distribution Network Reconfiguration Method Based on Compressed Switch Candidate Set by Haocheng DU, Shilong LI, Yuntao JU, Jinqi ZHANG

“…It transformed the model into a mixed-integer second-order conic planning problem by deterministically transforming the worst-case expectation and chance constraints in the objective function by using a dual transformation method. …”
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A new APSO-SPC method for parameter identification problem with uncertainty caused by random measurement errors by Peng Zhong, Xuanlong Wu, Li Zhu, Aohao Yang

“…In parameter identification problem, errors are common in measurement data, resulting in uncertainty in the identified parameters. Traditional deterministic methods cannot address this uncertainty. …”
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Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning by Qingli Liu, Yongjie Ma

“…Abstract In highly dynamic vehicular networking scenarios, when Vehicle-to-Infrastructure links and Vehicle-to-Vehicle links share spectrum resources, the traditional distributed resource allocation method lacks global optimization and fails to respond to environmental changes in a timely manner, which leads to low spectral efficiency of the system. …”
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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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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

“…The invertible mapping can increase the exploration efficiency and guide the training quality of agents. …”
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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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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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A Practical Cache Partitioning Method for Multi-Core Processor on a Commercial Safety-Critical Partitioned RTOS by Taeho Kim

“…While MCPs improve efficiency, they introduce nondeterministic behaviors due to resource contention and challenges for the safety of avionics systems. …”
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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

“…Electrification of transport is accelerating worldwide, raising new challenges for energy efficiency and control in electric vehicles. Reinforcement learning has emerged as a promising data-driven approach to address the complexity of real-time energy management. …”
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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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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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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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Numerical implementation of a stochastic differential equation of motion by Saúl Moisés Torres Murga

“…To do this, it was required to model Brownian motion since it efficiently represents the randomness of the phenomenon. …”
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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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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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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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