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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Double Critics and Double Actors Deep Deterministic Policy Gradient for Mobile Robot Navigation Using Adaptive Parameter Space Noise and Parallel Experience Replay by Wenjie Hu, Ye Zhou, Hann Woei Ho, Chun Zhang

“…This opens up new possibilities for improving the efficiency and effectiveness of other DRL methods.…”
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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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A Comparative Study Between Soft Actor-Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) Algorithms for Solar PV MPPT Control Under Partial Shading Conditions by Sampson E. Nwachukwu, Komla A. Folly, Kehinde O. Awodele

“…Simulation results show that SAC and DDPG algorithms outperform the P&O method under PSCs and varying irradiance levels. It is shown that the SAC algorithm exhibits superior performance, achieving high tracking efficiency and eliminating power oscillations near the PV MPP and GMPP compared to the DDPG method.…”
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An Improved Parameter Extraction Optimization Algorithm for RF Devices by Shengsen Yang, Zihan Xu, Kun Ren

“…It effectively addresses the limitations of deterministic algorithms in RF device parameter extraction optimization, such as low efficiency, sensitivity to initial values, and unstable convergence. …”
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Optimization of Parameters of a Vertical Ground Heat Exchanger in a Geothermal Heating System by Walery Jezierski, Piotr Rynkowski

“…Only factor <i>T</i>_w (<i>X</i>₄), with such a change, caused a decrease in the thermal efficiency of the tested heat exchanger by 44.22%. The parameters of the tested element of the geothermal heating system were also optimized according to the energy criterion using a numerical method in the Matlab environment. …”
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DetNet service share protection scheme based on SRv6 by Shuopeng LI, Juan FANG, Ken CHEN

“…To solve the problem that existing methods are difficult to achieve efficient deployment and service protection in deterministic network (DetNet), a DetNet service protection architecture based on SRv6 was proposed.By leveraging this architecture, a service-oriented DetNet protection scheme was designed and the share protection policy was formulated.With the aid of backup bandwidth share, the proposed heuristic based on shortest path optimized network resource utilization and delay.The simulation results show that the share protection algorithm achieves better performance than non-share algorithm.…”
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Robust Adaptive Fractional-Order PID Controller Design for High-Power DC-DC Dual Active Bridge Converter Enhanced Using Multi-Agent Deep Deterministic Policy Gradient Algorithm for... by Seyyed Morteza Ghamari, Daryoush Habibi, Asma Aziz

“…The Dual Active Bridge converter (DABC), known for its bidirectional power transfer capability and high efficiency, plays a crucial role in various applications, particularly in electric vehicles (EVs), where it facilitates energy storage, battery charging, and grid integration. …”
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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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Research on Fast Spectrum Resonance Calculation Method Based on Subgroup Method by YU Rongjun, ZHAO Shouzhi, YANG Rui, SONG Yingyun, ZHANG Chong, HUO Xingkai

“…With the rapid development of digital technology and the rapid improvement of computer performance, the high-fidelity deterministic transport calculation method represented by the method of characteristics (MOC) has received extensive attention in numerical reactor projects at home and abroad. …”
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Optimization research on UAV semantic communication system based on SVD-MADRL by Yibo Yang, Yahe Tan, Liu Liu

“…In addition, compared with traditional algorithms, the semantic communication optimization model constructed in this study performs the best in terms of total reward, data collection efficiency, and accuracy. It has strong stability and convergence ability, with an accuracy close to 1, significantly improving the accuracy and efficiency of unmanned aerial vehicle communication transmission. …”
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Digital Twin and TD3-Enabled Optimization of xEV Energy Management in Vehicle-to-Grid Networks by Irum Saba, Abdulraheem H. Alobaidi, Sultan Alghamdi, Muhammad Tariq

“…This paper addresses these challenges by proposing an advanced ESS framework that integrates digital twin (DT) technology with the twin-delayed deep deterministic policy gradient (TD3) algorithm, a state-of-the-art reinforcement learning method derived from the deep deterministic policy gradient (DDPG). …”
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A novel approach to pseudorandom number generation using Hamiltonian conservative chaotic systems by Vinod Patidar, Tanu Singh

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Decentralized Voltage and Var Control of Active Distribution Network Based on Parameter-Sharing Deep Reinforcement Learning by Binqiao Zhang, Changlin He

“…This work introduces a Parameter Sharing - twin-delay deep deterministic policy gradient (PS-TD3) method for carrying out decentralized voltage and var control in active distribution networks. …”
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Tracking Control of CSTRs Based on Improved OU Noise and the TD3 Algorithm by Hongyan Shi, Xiaofei Wu, Guogang Wang

“…To address this challenge, this study proposes a novel deep reinforcement learning (DRL) approach that integrates an improved Ornstein-Uhlenbeck (IOU) noise into the twin delayed deep deterministic policy gradient (TD3) algorithm. This method is applied to the tracking control of continuous stirred tank reactors (CSTRs). …”
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A DRL-based optimization method for microgrid operation by ZENG Lei, DING Quan, CHEN Xiaoyu, YUE Xianya

“…Third， a penalty term for high-proportion erroneous actions is incorporated into the reward function to constrain the output of each device within a reasonable range， mitigating the risk of insufficient safety guarantees inherent in reinforcement learning methods. Finally， simulation results demonstrate that， compared to the deep deterministic policy gradient （DDPG） algorithm， the proposed method achieves superior economic efficiency and stability， with economic costs closer to those of ideal deterministic optimization methods.…”
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Research on Random Discrete Ordinates Method Based on Quasi-Monte Carlo Integration Technique by DAI Tao, XU Longfei, LI Baiwen, SHEN Huayun, HU Yuan, MA Ruiyao

“…Owing to its superior computational accuracy and efficiency compared to alternative numerical approaches, the SN method has been extensively adopted in neutron transport applications, particularly in nuclear reactor design and radiation shielding optimization. …”

OPERATIONAL ANALYSIS OF COMPLEX MEDICAL STATES BY PHOTONICS METHODS by A. I. Larkin, K. A. Trukhanov

“…Such an analysis is carried out by the means of vector-matrix multiplication using laser photonics methods. It is significant that with the widening of the range of probability algorithms, it is possible to preserve certain advantages of the holographic method: multidimensionality, efficiency, high information capacity and speed, visibility and flexibility of the result presentation. …”
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Cumulative Distribution-Based Method for Pavement Performance Modeling by Chenhao Tu, Wenya Ye, Rui Zhang, Xudong Qian, Qun Yang

“…Predicting future pavement conditions accurately and efficiently helps determine the optimal maintenance time, select appropriate measures, and allocate rehabilitation funds effectively. …”
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The CADIS-DXTRAN method in NECP-MCX for Radiation Shielding Simulations by Huang Zhanpeng, Cao Liangzhi, He Qingming, Wu Hongchun

“…The results of the labyrinth problem reveal that the CADIS-DXTRAN method is highly efficient with a Figure of Merit (FOM) value 45 times that of DXTRAN and 17 times that of the CADIS method. …”
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