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Journal articles on the topic 'RL parameters'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Ger, Yoav, Eliya Nachmani, Lior Wolf, and Nitzan Shahar. "Harnessing the flexibility of neural networks to predict dynamic theoretical parameters underlying human choice behavior." PLOS Computational Biology 20, no. 1 (2024): e1011678. http://dx.doi.org/10.1371/journal.pcbi.1011678.

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Reinforcement learning (RL) models are used extensively to study human behavior. These rely on normative models of behavior and stress interpretability over predictive capabilities. More recently, neural network models have emerged as a descriptive modeling paradigm that is capable of high predictive power yet with limited interpretability. Here, we seek to augment the expressiveness of theoretical RL models with the high flexibility and predictive power of neural networks. We introduce a novel framework, which we term theoretical-RNN (t-RNN), whereby a recurrent neural network is trained to p

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Erdei, Éva, Pál Pepó, János Csapó, Szilárd Tóth, and Béla Szabó. "Sweet sorghum (Sorghum dochna L.) restorer lines effects on nutritional parameters of stalk juice." Acta Agraria Debreceniensis, no. 36 (November 2, 2009): 51–56. http://dx.doi.org/10.34101/actaagrar/36/2792.

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Sweet sorghum can be utilized for bioethanol production because it has high sugar content (14-17%). We determined the most important nutritional values of 5 silo type sorghum lines in waxy and full maturation. The examined restorer lines were: RL 4, RL 9, RL 15, RL 18, K 1. The following nutritional parameters were examined: dry material content, refractometric total sugar content, reducing sugar content. In waxy maturation 73.85-87.37% of dry matter in stalk juice makes the total sugar. Dry material content, total and reducing sugar content of stalkdecreases from waxy mature to full maturatio

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Xu, Peng, Guoping Qian, Chao Zhang, et al. "Influence of the Surface Texture Parameters of Asphalt Pavement on Light Reflection Characteristics." Applied Sciences 13, no. 23 (2023): 12824. http://dx.doi.org/10.3390/app132312824.

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The optical reflection characteristics of asphalt pavement have an important influence on road-lighting design, and the macrotexture and microtexture of asphalt pavement significantly affect its reflection characteristics. To investigate the impact of texture parameters on the retroreflection coefficient of asphalt pavement, the texture indices of rutted plate specimens and field asphalt pavement were obtained by a pavement texture tester, including the macrotexture surface area (S1), microtexture surface area (S2), macrotexture distribution density (D1), microtexture distribution density (D2)

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Ali, Anwer, Mofeed Rashid, Bilal Alhasnawi, Vladimír Bureš, and Peter Mikulecký. "Reinforcement-Learning-Based Level Controller for Separator Drum Unit in Refinery System." Mathematics 11, no. 7 (2023): 1746. http://dx.doi.org/10.3390/math11071746.

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The Basrah Refinery, Iraq, similarly to other refineries, is subject to several industrial constraints. Therefore, the main challenge is to optimize the parameters of the level controller of the process unit tanks. In this paper, a PI controller is designed for these important processes in the Basrah Refinery, which is a separator drum (D5204). Furthermore, the improvement of the PI controller is achieved under several constraints, such as the inlet liquid flow rate to tank (m2) and valve opening in yi%, by using two different techniques: the first one is conducted using a closed-Loop PID auto

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Wang, Lei, Atsushi Sekimoto, Yuto Takehara, Yasunori Okano, Toru Ujihara, and Sadik Dost. "Optimal Control of SiC Crystal Growth in the RF-TSSG System Using Reinforcement Learning." Crystals 10, no. 9 (2020): 791. http://dx.doi.org/10.3390/cryst10090791.

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We have developed a reinforcement learning (RL) model to control the melt flow in the radio frequency (RF) top-seeded solution growth (TSSG) process for growing more uniform SiC crystals with a higher growth rate. In the study, the electromagnetic field (EM) strength is controlled by the RL model to weaken the influence of Marangoni convection. The RL model is trained through a two-dimensional (2D) numerical simulation of the TSSG process. As a result, the growth rate under the control of the RL model is improved significantly. The optimized RF-coil parameters based on the control strategy for

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Moriyama, Takumi, Ryosuke Koishi, Kouhei Kimura, Satoru Kishida, and Kentaro Kinoshita. "Extraction of Filament Properties in Resistive Random Access Memory (ReRAM) Consisting of Binary-Transition-Metal-Oxides." Advances in Science and Technology 95 (October 2014): 84–90. http://dx.doi.org/10.4028/www.scientific.net/ast.95.84.

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Which parameter dominantly decides the value of time required to reset ReRAM (treset) among possible parameters, the value of a low resistance (RL), voltage to induce reset (Vreset), and temperature to induce reset (Treset)? Although to answer this question is important to achieve faster resistive switching, detailed correlations between the parameters are still unclear. In this paper, we extracted treset, Vreset, RL and Treset at the same time by combining two electrical measurements. As a result, we found a clear correlation between Vreset, RL, and Treset, meaning that each parameter can not

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S. Manjunatha. "A Novel ML-Driven Approach to Enhance CRN Performance under Varying Network Parameters." Journal of Electrical Systems 20, no. 11s (2024): 1590–602. https://doi.org/10.52783/jes.7547.

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This paper explores RL and DRL techniques for spectrum allocation in the context of CRNs, with consideration of difficulties like spectrum utilization and network performance in changing conditions. The proposed improved spectrum management model integrates RL with model-based prediction as a way of improving decision making. The results of the experiment prove that the identified approach allows for achieving an average level of accuracy of 96%, and a loss rate of 0.20, as well as of precision of 92% to 0.95. In addition, recall was extended from 0.85 to 0.90, and the F1 score was at 0.90, wh

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Liu, Yang, and Lujun Zhou. "Modeling RL Electrical Circuit by Multifactor Uncertain Differential Equation." Symmetry 13, no. 11 (2021): 2103. http://dx.doi.org/10.3390/sym13112103.

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The symmetry principle of circuit system shows that we can equate a complex structure in the circuit network to a simple circuit. Hence, this paper only considers a simple series RL circuit and first presents an uncertain RL circuit model based on multifactor uncertain differential equation by considering the external noise and internal noise in an actual electrical circuit system. Then, the solution of uncertain RL circuit equation and the inverse uncertainty distribution of solution are derived. Some applications of solution for uncertain RL circuit equation are also investigated. Finally, t

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Zhang, Zhitong, Xu Chang, Hongxu Ma, Honglei An, and Lin Lang. "Model Predictive Control of Quadruped Robot Based on Reinforcement Learning." Applied Sciences 13, no. 1 (2022): 154. http://dx.doi.org/10.3390/app13010154.

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For the locomotion control of a legged robot, both model predictive control (MPC) and reinforcement learning (RL) demonstrate powerful capabilities. MPC transfers the high-level task to the lower-level joint control based on the understanding of the robot and environment, model-free RL learns how to work through trial and error, and has the ability to evolve based on historical data. In this work, we proposed a novel framework to integrate the advantages of MPC and RL, we learned a policy for automatically choosing parameters for MPC. Unlike the end-to-end RL applications for control, our meth

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Rezaei-Shoshtari, Sahand, Charlotte Morissette, Francois R. Hogan, Gregory Dudek, and David Meger. "Hypernetworks for Zero-Shot Transfer in Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9579–87. http://dx.doi.org/10.1609/aaai.v37i8.26146.

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In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hyperne

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Mahajan, Yukti, and Harish Nagar. "Riemann-Liouville fractional operator on S-function." Journal of Interdisciplinary Mathematics 28, no. 4 (2025): 1689–94. https://doi.org/10.47974/jim-2230.

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This paper provides a brief overview of the Riemann-Liouville fractional operator (Rl- operator) on S-Function. The key findings illustrate the impact of these operators on the parameters, showing that both the Rl- fractional operators of differential and integrals involving the S-Function can be represented using S-Function. These results can be used to derive specific cases by adjusting the parameters accordingly.

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Khlebnikova, D. A., A. A. Lobova, O. N. Aladina, and M. Yu Cherednichenko. "The impact of light spectral composition on the in vitro growth of summer savory (Satureja hortensis L.) plants." Vegetable crops of Russia, no. 6 (December 18, 2019): 72–75. http://dx.doi.org/10.18619/2072-9146-2019-6-72-75.

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Relevance. Summer savory (Satureja hortensis L.) is an annual herbaceous plant whose essential oil and extracts are used in medicine, perfumery and the food industry.Methods. The article presents the results of studying the effect of the ratio of blue (BL) and red light (RL) in the total spectrum of LED lamps on the morphometric parameters of plants of summer savory varieties Gnom and Perechny aromat in vitro. For in vitro culture, seeds were sterilized with 5% NaCl solution for 10 minutes, placed in Petri dishes with Murashige and Skoog culture medium (MS). Aseptic seedlings at the age of 4-5

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Qurrat-ul-Ain and Yasir Hussain. "Parametric Variations in Urdu and French: Examining Null Subject Parameters." Regional Lens 4, no. 1 (2025): 105–13. https://doi.org/10.62997/rl.2025.41030.

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Universal Grammar principles indicate that our natural linguistic abilities comprise a restricted collection of fundamental principles shared by all languages, with distinctions arising from parameters. Essentially, these parameters determine the syntactic variations between languages within the defined constraints of a specific parameter. This research explores a particular parametric difference between Urdu and French, focusing on the null subject parameter and assessing whether Urdu and French function as null subjects or pro-drop languages. It examines the interaction between a mechanic an

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Yahya Saifan, Hamed, Mostafa Abbas Shalaby, Khaled Abo-EL-Sooud, M. A. Tony, and Aya M. Yassin. "English Effects of sodium butyrate and rosemary leaves on performance, biochemical parameters, immune status, and carcass traits of broiler chickens." International Journal of Biological Research 11, no. 2 (2024): 67–74. http://dx.doi.org/10.14419/1w9dk133.

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Effects of sodium butyrate (SB) and rosemary leaves (RL) on growth performance, biochemical profile, immune status, and carcass traits of broiler were evaluated. Fifty-one-day old Hubbard chicks (unsexed) were purchased from Al-Ahram Company for Poultry, Egypt and reared on floor pens in a private farm. The chicks were weighed on arrival and assigned at random into five equal groups, with four replicates each (25 chicks/replicate). Group 1 was fed on a broiler diet without any additions and used as a control. The diets of groups 2 and 3 were supplemented with 500 g/ton SB and 4 kg/ton RL, resp

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Ajani, Oladayo S., Sung-ho Hur, and Rammohan Mallipeddi. "Evaluating Domain Randomization in Deep Reinforcement Learning Locomotion Tasks." Mathematics 11, no. 23 (2023): 4744. http://dx.doi.org/10.3390/math11234744.

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Domain randomization in the context of Reinforcement learning (RL) involves training RL agents with randomized environmental properties or parameters to improve the generalization capabilities of the resulting agents. Although domain randomization has been favorably studied in the literature, it has been studied in terms of varying the operational characters of the associated systems or physical dynamics rather than their environmental characteristics. This is counter-intuitive as it is unrealistic to alter the mechanical dynamics of a system in operation. Furthermore, most works were based on

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Tarun, Parmar. "Dynamic Recipe Adjustment in Industrial Processes: Exploring Reinforcement Learning Approaches." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 11, no. 1 (2025): 1–7. https://doi.org/10.5281/zenodo.14836371.

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Reinforcement learning (RL) has emerged as a promising approach for optimizing recipes and manufacturing processes in various industries. This review explores the application of RL techniques for dynamic recipe adjustment and discusses the key concepts, algorithms, and challenges. RL fundamentals, including Q-learning, policy gradients, and actor-critic methods, are reviewed, explaining how these algorithms can model recipes as RL environments. Potential state representations, action spaces, and reward functions are examined, considering factors such as ingredient quantities, process parameter

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Kabashnikova, Liudmila, Irina Domanskaya, Olga Molchan, et al. "Impact of Light with Different Spectra on the Photosynthetic Activity of Cucumber Plants under Fusarium Wilt." Global Journal Of Botanical Science 10 (December 21, 2022): 55–63. http://dx.doi.org/10.12974/2311-858x.2022.10.07.

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The photosynthetic activity of 28-day-old cucumber plants of the Kustovy variety formed under LED illumination with the predominance of red light (RL) or far red light (FRL) and infected with the fungus Fusarium oxisporum sp. (F.ox.) was studied. The predominance of RL or FRL contributed to an increase in the content of chlorophyll and carotenoids per dry leaf weight compared to the plants grown under white light (WL). In the infected plants grown under WL and RL regimes, an increase in the total content of chlorophyll and carotenoids was observed relative to healthy plants, and a decrease in

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18

Peng, Zhiyong, Changlin Han, Yadong Liu, and Zongtan Zhou. "Weighted Policy Constraints for Offline Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9435–43. http://dx.doi.org/10.1609/aaai.v37i8.26130.

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Offline reinforcement learning (RL) aims to learn policy from the passively collected offline dataset. Applying existing RL methods on the static dataset straightforwardly will raise distribution shift, causing these unconstrained RL methods to fail. To cope with the distribution shift problem, a common practice in offline RL is to constrain the policy explicitly or implicitly close to behavioral policy. However, the available dataset usually contains sub-optimal or inferior actions, constraining the policy near all these actions will make the policy inevitably learn inferior behaviors, limiti

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Khan, Owais, Sana Ullah, Muzammil Khan, and Han-Chieh Chao. "RL-BMAC: An RL-Based MAC Protocol for Performance Optimization in Wireless Sensor Networks." Information 16, no. 5 (2025): 369. https://doi.org/10.3390/info16050369.

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Applications of wireless sensor networks have significantly increased in the modern era. These networks operate on a limited power supply in the form of batteries, which are normally difficult to replace on a frequent basis. In wireless sensor networks, sensor nodes alternate between sleep and active states to conserve energy through different methods. Duty cycling is among the most commonly used methods. However, it suffers from problems like unnecessary idle listening, extra energy consumption, and packet drop rate. A Deep Reinforcement Learning-based B-MAC protocol called (RL-BMAC) has been

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Puspitasari, Annisa Anggun, and Byung Moo Lee. "A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications." Sensors 23, no. 5 (2023): 2554. http://dx.doi.org/10.3390/s23052554.

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A reconfigurable intelligent surface (RIS) is a development of conventional relay technology that can send a signal by reflecting the signal received from a transmitter to a receiver without additional power. RISs are a promising technology for future wireless communication due to their improvement of the quality of the received signal, energy efficiency, and power allocation. In addition, machine learning (ML) is widely used in many technologies because it can create machines that mimic human mindsets with mathematical algorithms without requiring direct human assistance. Meanwhile, it is nec

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Satish Chitimoju. "The role of reinforcement learning in autonomous architectural optimization and energy efficiency." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 3358–73. https://doi.org/10.30574/wjarr.2024.24.3.3907.

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The surge of worldwide energy requirements and the necessity of sustainable architecture have made artificial intelligence optimization methods more important than ever. Reinforcement Learning (RL) is a fundamental method to develop better energy performance in architecture through data-based adaptive decision systems. Real-time operation capabilities of RL models allow them to change architectural parameters dynamically, optimizing energy consumption and building performance output. Autonomous architectural design benefits from applying RL technology, which enhances sustainability, improves m

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Satish, Chitimoju. "The role of reinforcement learning in autonomous architectural optimization and energy efficiency." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 3358–73. https://doi.org/10.5281/zenodo.15266720.

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The surge of worldwide energy requirements and the necessity of sustainable architecture have made artificial intelligence optimization methods more important than ever. Reinforcement Learning (RL) is a fundamental method to develop better energy performance in architecture through data-based adaptive decision systems. Real-time operation capabilities of RL models allow them to change architectural parameters dynamically, optimizing energy consumption and building performance output. Autonomous architectural design benefits from applying RL technology, which enhances sustainability, improves m

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Zhang, Jingwei, Zenan Yang, Kun Ding, et al. "Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics." Energies 15, no. 18 (2022): 6567. http://dx.doi.org/10.3390/en15186567.

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Currently, the accuracy of modeling a photovoltaic (PV) array for fault diagnosis is still unsatisfactory due to the fact that the modeling accuracy is limited by the accuracy of extracted model parameters. In this paper, the modeling of a PV array based on multi-agent deep reinforcement learning (RL) using the residuals of I–V characteristics is proposed. The environment state based on the high dimensional residuals of I–V characteristics and the corresponding cooperative reward is presented for the RL agents. The actions of each agent considering the damping amplitude are designed. Then, the

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Abed, Farook Nehad. "Alumina Nano Powder Impact on Electrical Discharge Machining of Titanium Alloy Wire." Materials Science Forum 1079 (December 26, 2022): 57–65. http://dx.doi.org/10.4028/p-50kx64.

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WEDM is an unconventional method of thermal machining that produces products with irregular shapes. The results of milling titanium (TI-6242) under various machining conditions that affect the WEDM process are provided. Pulse on time (Ton), pulse off time (Toff), peak current (Ip), voltage (V), wire tension (Wt), and wire feed are all considered machining parameters (Wf). They are established using an experimental design and the Box–Behnken approach to optimize the machining factors. The optimization goal is to attain the highest Material Removal Rate (MRR) and the least amount of recast layer

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Suchithra, Jude, Duane Robinson, and Amin Rajabi. "Hosting Capacity Assessment Strategies and Reinforcement Learning Methods for Coordinated Voltage Control in Electricity Distribution Networks: A Review." Energies 16, no. 5 (2023): 2371. http://dx.doi.org/10.3390/en16052371.

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Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation and other operational requirements. The solution to this dilemma is to undertake a hosting capacity (HC) study to identify the maximum penetration limit of rooftop PV generation and take necessary actions to enhance the HC of the network. This paper presents a comprehensive rev

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Pandit, Paresh B., Kee H. Pyon, Sherry E. Courtney, Sandra E. England, and Robert H. Habib. "Lung resistance and elastance in spontaneously breathing preterm infants: effects of breathing pattern and demographics." Journal of Applied Physiology 88, no. 3 (2000): 997–1005. http://dx.doi.org/10.1152/jappl.2000.88.3.997.

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Reported values of lung resistance (Rl) and elastance (El) in spontaneously breathing preterm neonates vary widely. We hypothesized that this variability in lung properties can be largely explained by both inter- and intrasubject variability in breathing pattern and demographics. Thirty-three neonates receiving nasal continuous positive airway pressure [weight 606–1,792 g, gestational age (GA) of 25–33 wk, 2–49 days old] were studied. Transpulmonary pressure was measured by esophageal manometry and airway flow by face mask pneumotachography. Breath-to-breath changes in Rl and El in each infant

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Lu, Chao, Jie Huang, and Jianwei Gong. "Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters." PROMET - Traffic&Transportation 28, no. 4 (2016): 371–81. http://dx.doi.org/10.7307/ptt.v28i4.1830.

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Reinforcement Learning (RL) has been proposed to deal with ramp control problems under dynamic traffic conditions; however, there is a lack of sufficient research on the behaviour and impacts of different learning parameters. This paper describes a ramp control agent based on the RL mechanism and thoroughly analyzed the influence of three learning parameters; namely, learning rate, discount rate and action selection parameter on the algorithm performance. Two indices for the learning speed and convergence stability were used to measure the algorithm performance, based on which a series of simu

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Ahlam, K. Alaila, M. Salih Sami, and A. Abdulrraziq Ahmed. "Biological activity of Arum Cyreniacum (Araceae) and potential use as Bioherbicide." Biological activity of Arum Cyreniacum (Araceae) and potential use as Bioherbicide 3, no. 1 (2021): 58–67. https://doi.org/10.36811/ijpsh.2021.110032.

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The present investigation aims was carried out to study the biological activity of aqueous extract and shoot crude powder of Arum cyreniacum (ACSAE and ACSCP) on some germination and growth parameters (germination bioassay experiment) besides major physiological, and biochemical processes (pot experiment) In Hordeum vulgare (crop species) and Phalaris minor (weed species) of different concentrations of A. cyreniacum on germination percentage (GP), coleoptile (CL) and radicle (RL) lengths, seedling shoot and root length seedling fresh and dry weight, some nutrients (N, K, Na, Cu, Fe, and Ni ),

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Garcia, Gonzalo, Azim Eskandarian, Ernesto Fabregas, Hector Vargas, and Gonzalo Farias. "Cooperative Formation Control of a Multi-Agent Khepera IV Mobile Robots System Using Deep Reinforcement Learning." Applied Sciences 15, no. 4 (2025): 1777. https://doi.org/10.3390/app15041777.

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The increasing complexity of autonomous vehicles has exposed the limitations of many existing control systems. Reinforcement learning (RL) is emerging as a promising solution to these challenges, enabling agents to learn and enhance their performance through interaction with the environment. Unlike traditional control algorithms, RL facilitates autonomous learning via a recursive process that can be fully simulated, thereby preventing potential damage to the actual robot. This paper presents the design and development of an RL-based algorithm for controlling the collaborative formation of a mu

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Lakhani, Ayub I., Myisha A. Chowdhury, and Qiugang Lu. "Stability-preserving automatic tuning of PID control with reinforcement learning." Complex Engineering Systems 2 (2022): 3. http://dx.doi.org/10.20517/ces.2021.15.

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Proportional-Integral-Derivative (PID) control has been the dominant control strategy in the process industry due to its simplicity in design and effectiveness in controlling a wide range of processes. However, most traditional PID tuning methods rely on trial and error for complex processes where insights about the system are limited and may not yield the optimal PID parameters. To address the issue, this work proposes an automatic PID tuning framework based on reinforcement learning (RL), particularly the deterministic policy gradient (DPG) method. Different from existing studies on using RL

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Kerger, H., A. G. Tsai, D. J. Saltzman, R. M. Winslow, and M. Intaglietta. "Fluid resuscitation with O2 vs. non-O2 carriers after 2 h of hemorrhagic shock in conscious hamsters." American Journal of Physiology-Heart and Circulatory Physiology 272, no. 1 (1997): H525—H537. http://dx.doi.org/10.1152/ajpheart.1997.272.1.h525.

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Efficacy of a cell-free o-raffinose cross-linked and oligomerized hemoglobin (Hemo-link) solution in restoring macro- and microcirculatory conditions after 2 h of hemorrhagic shock (40 mmHg) was compared with conventional treatment with autologous whole blood, Ringer lactate (RL), and Dextran 70. Studies were conducted in the dorsal skinfold microcirculation of conscious hamsters. Initial infusion was equivalent to shed blood volume (SBV) for RL and 50% of SBV for remaining solutions. After 2 h all animals received blood at 50% of SBV. Vessel diameter, functional capillary density, microvascul

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Generoso, Tarcila N., Mauro A. Martinez, Genelício C. Rocha, and Paulo J. Hamakawa. "Water magnetization and phosphorus transport parameters in the soil." Revista Brasileira de Engenharia Agrícola e Ambiental 21, no. 1 (2017): 9–13. http://dx.doi.org/10.1590/1807-1929/agriambi.v21n1p9-13.

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ABSTRACT There are scientific studies describing changes in properties of the water when subjected to the action of a magnetic field, which may favor the availability of some nutrients in the soil solution. Some nutrients, although they are essential to the process of crop development, can be sources of pollution for watercourses and soil. The aim of this study was to evaluate the effect of water magnetization on transport parameters of the phosphate ion in a Red Latosol (RL) and in a Quartzarenic Neosol (QN). Saturated leaching columns were connected to bottles containing KH2PO4 solutions. In

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Al-Rawi, Hasan A. A., Kok-Lim Alvin Yau, Hafizal Mohamad, Nordin Ramli, and Wahidah Hashim. "Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks." Scientific World Journal 2014 (2014): 1–22. http://dx.doi.org/10.1155/2014/960584.

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Cognitive radio (CR) enables unlicensed users (or secondary users, SUs) to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs). Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research are

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Li, Beichen, Yiwei Hu, Paul Guerrero, et al. "Procedural Material Generation with Reinforcement Learning." ACM Transactions on Graphics 43, no. 6 (2024): 1–14. http://dx.doi.org/10.1145/3687979.

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Modern 3D content creation heavily relies on procedural assets. In particular, procedural materials are ubiquitous in the industry, but their manipulation remains challenging. Previous work [Hu et al. 2023] conditionally generates procedural graphs that match a given input image. However, the parameter generation step limits how accurately the generated graph matches the input image, due to a reliance on supervision with scarcely available procedural data. We propose to improve parameter prediction accuracy for image-conditioned procedural material generation by leveraging reinforcement learni

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Li, Shaodong, Xiaogang Yuan, and Jie Niu. "Robotic Peg-in-Hole Assembly Strategy Research Based on Reinforcement Learning Algorithm." Applied Sciences 12, no. 21 (2022): 11149. http://dx.doi.org/10.3390/app122111149.

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To improve the robotic assembly effects in unstructured environments, a reinforcement learning (RL) algorithm is introduced to realize a variable admittance control. In this article, the mechanisms of a peg-in-hole assembly task and admittance model are first analyzed to guide the control strategy and experimental parameters design. Then, the admittance parameter identification process is defined as the Markov decision process (MDP) problem and solved with the RL algorithm. Furthermore, a fuzzy reward system is established to evaluate the action–state value to solve the complex reward establis

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Huang, Zeyuan, Gang Chen, Yue Shen, Ruiquan Wang, Chuankai Liu, and Long Zhang. "An Obstacle-Avoidance Motion Planning Method for Redundant Space Robot via Reinforcement Learning." Actuators 12, no. 2 (2023): 69. http://dx.doi.org/10.3390/act12020069.

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On-orbit operation tasks require the space robot to work in an unstructured dynamic environment, where the end-effector’s trajectory and obstacle avoidance need to be guaranteed simultaneously. To ensure the completability and safety of the tasks, this paper proposes a new obstacle-avoidance motion planning method for redundant space robots via reinforcement learning (RL). First, the motion planning framework, which combines RL with the null-space motion for redundant space robots, is proposed according to the decomposition of joint motion. Second, the RL model for null-space obstacle avoidanc

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Lu, Chuang, and Xiangtao Zhuan. "Adaptive Control for Virtual Synchronous Generator Parameters Based on Soft Actor Critic." Sensors 24, no. 7 (2024): 2035. http://dx.doi.org/10.3390/s24072035.

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This paper introduces a model-free optimization method based on reinforcement learning (RL) aimed at resolving the issues of active power and frequency oscillations present in a traditional virtual synchronous generator (VSG). The RL agent utilizes the active power and frequency response of the VSG as state information inputs and generates actions to adjust the virtual inertia and damping coefficients for an optimal response. Distinctively, this study incorporates a setting-time term into the reward function design, alongside power and frequency deviations, to avoid prolonged system transients

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Chandran, K. Prabhu, Sesham Anand, Subramanya V. Odeyar, and Karra Basheeba Rani. "REINFORCEMENT LEARNING FOR ADAPTIVE SIGNAL PROCESSING FOR CONTEXT AWARENESS IN 5G COMMUNICATION TECHNOLOGY." ICTACT Journal on Communication Technology 15, no. 3 (2024): 3314–19. http://dx.doi.org/10.21917/ijct.2024.0492.

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The advent of 5G communication technology has revolutionized wireless communication with its high bandwidth, ultra-low latency, and massive connectivity features. However, the dynamic nature of user behavior and environmental changes poses significant challenges in optimizing signal processing for context awareness. Adaptive signal processing (ASP) offers a promising solution, but traditional methods struggle to effectively handle real-time, context-sensitive demands. In this research, we propose a novel reinforcement learning (RL)-based framework for adaptive signal processing that enhances c

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Khawaja, Wahab, Qasim Yaqoob, and Ismail Guvenc. "RL-Based Detection, Tracking, and Classification of Malicious UAV Swarms through Airborne Cognitive Multibeam Multifunction Phased Array Radar." Drones 7, no. 7 (2023): 470. http://dx.doi.org/10.3390/drones7070470.

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Detecting, tracking, and classifying unmanned aerial vehicles (UAVs) in a swarm presents significant challenges due to their small and diverse radar cross-sections, multiple flight altitudes, velocities, and close trajectories. To overcome these challenges, adjustments of the radar parameters and/or position of the radar (for airborne platforms) are often required during runtime. The runtime adjustments help to overcome the anomalies in the detection, tracking, and classification of UAVs. The runtime adjustments are performed either manually or through fixed algorithms, each of which can have

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Eppa, Akhilesh Reddy. "Optimizing Autonomous Systems through Reinforcement Learning: The Role of Linear Regression, Random Forest, and Support Vector Machines in Decision Making." International Journal of Computer Science and Data Engineering 2, no. 1 (2025): 1–21. https://doi.org/10.55124/csdb.v2i2.249.

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Reinforcement Learning (RL), a powerful paradigm for decision-making in autonomous systems, has enabled agents to learn the optimum policies through trial and error in dynamic environments. This paper explores the integration of RL within autonomous systems, emphasizing how various machine learning algorithms Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Machines (SVM) can assist in predicting agent performance and optimizing training processes. These algorithms are employed to analyze key input parameters, including sensor accuracy (%), processing power (GHz), and

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Wu, Kunlin, Ding Zhang, Minghua Liu, Qi Lin, and Bing-Chiuan Shiu. "A Study on the Improvement of Using Raw Lacquer and Electrospinning on Properties of PVP Nanofilms." Nanomaterials 10, no. 9 (2020): 1723. http://dx.doi.org/10.3390/nano10091723.

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Raw lacquer (RL), ethanol being used as the solvent, was added to polyvinyl pyrrolidone (PVP) and then electrospun into RL/PVP nanofilms. Manufacturing parameters such as RL/PVP ratio, voltage, flow velocity, needle type, and the distance between syringe and the collection board were systematically investigated. A scanning electronic microscope (SEM) was used to observe the surface morphology of nanofilms; the block drop method was used to measure the water contact angle; the mechanical properties of RL/PVP nanofilms of different proportions were tested by universal material testing machine; a

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Sun, Xinyu, Zhikun Zhao, Lili Wei, et al. "RL-SeqISP: Reinforcement Learning-Based Sequential Optimization for Image Signal Processing." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (2024): 5025–33. http://dx.doi.org/10.1609/aaai.v38i5.28307.

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Hardware image signal processing (ISP), aiming at converting RAW inputs to RGB images, consists of a series of processing blocks, each with multiple parameters. Traditionally, ISP parameters are manually tuned in isolation by imaging experts according to application-specific quality and performance metrics, which is time-consuming and biased towards human perception due to complex interaction with the output image. Since the relationship between any single parameter’s variation and the output performance metric is a complex, non-linear function, optimizing such a large number of ISP parameters

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Vieira, Jairo V., Giovani Olegario da Silva, and Leonardo S. Boiteux. "Genetic parameter and correlation estimates of processing traits in half-sib progenies of tropical-adapted carrot germplasm." Horticultura Brasileira 30, no. 1 (2012): 7–11. http://dx.doi.org/10.1590/s0102-05362012000100002.

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The estimate of the genetic parameters associated with processing (fresh-cut) traits, including root length (RL), is crucial for carrot breeding programs in tropical areas. The cultivar Alvorada is an important germplasm due to its resistance to nematodes, leaf blight, heat-tolerance, and high carotenoid content. Seventy-four 'Alvorada' half-sib progenies were evaluated during the summer of 2005 in the Federal District, Brazil, in a randomized complete block design with three replications. Thirteen competitive plants in each block were randomly selected and evaluated and/or classified for RL a

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Coelho-Magalhães, Tiago, Christine Azevedo Coste, and Henrique Resende-Martins. "A Novel Functional Electrical Stimulation-Induced Cycling Controller Using Reinforcement Learning to Optimize Online Muscle Activation Pattern." Sensors 22, no. 23 (2022): 9126. http://dx.doi.org/10.3390/s22239126.

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This study introduces a novel controller based on a Reinforcement Learning (RL) algorithm for real-time adaptation of the stimulation pattern during FES-cycling. Core to our approach is the introduction of an RL agent that interacts with the cycling environment and learns through trial and error how to modulate the electrical charge applied to the stimulated muscle groups according to a predefined policy and while tracking a reference cadence. Instead of a static stimulation pattern to be modified by a control law, we hypothesized that a non-stationary baseline set of parameters would better a

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Vaishnav, Avani S., Philip Saville, Steven McAnany, et al. "Retrospective Review of Immediate Restoration of Lordosis in Single-Level Minimally Invasive Transforaminal Lumbar Interbody Fusion: A Comparison of Static and Expandable Interbody Cages." Operative Neurosurgery 18, no. 5 (2019): 518–23. http://dx.doi.org/10.1093/ons/opz240.

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Abstract BACKGROUND Sagittal alignment is an important consideration in spine surgery. The literature is conflicted regarding the effect of minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) on sagittal parameters and the role of expandable cage technology. OBJECTIVE To compare lordosis generated by static and expandable cages and to determine what factors affect postoperative sagittal parameters. METHODS Preoperative regional lordosis (RL), segmental lordosis (SL), and posterior disc height (PDH) were compared to postoperative values in single-level MI-TLIF performed using ex

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Himanshu, Kumar, and Kumar Ajeet. "To Study the Effect of Two Different Regimes of Oxytocin on Haemodynamic Parameters in Patients Undergoing Elective Caesarean Section." International Journal of Pharmaceutical and Clinical Research 16, no. 4 (2024): 459–66. https://doi.org/10.5281/zenodo.11177646.

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<strong>Introduction: </strong>Uncertainty exists over the ideal oxytocin dosage during elective caesarean sections. An insufficient amount of oxytocin might lead to insufficient uterine tone and increased uterine haemorrhage, whilst an excessive dose can have negative cardiovascular consequences including tachycardia and hypotension. To gauge the haemodynamic alterations and uterine contraction in patients undergoing elective caesarean sections, we examined two different oxytocin regimens. <strong>Aims/ Objective: </strong>To compare the heart rate, systolic blood pressure, dia

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Okutu, John Kwadey, Nana K. Frempong, Simon K. Appiah, and Atinuke O. Adebanji. "A New Generated Family of Distributions: Statistical Properties and Applications with Real-Life Data." Computational and Mathematical Methods 2023 (September 19, 2023): 1–18. http://dx.doi.org/10.1155/2023/9325679.

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Several standard distributions can be used to model lifetime data. Nevertheless, a number of these datasets from diverse fields such as engineering, finance, the environment, biological sciences, and others may not fit the standard distributions. As a result, there is a need to develop new distributions that incorporate a high degree of skewness and kurtosis while improving the degree of goodness-of-fit in empirical distributions. In this study, by applying the T-X method, we proposed a new flexible generated family, the Ramos-Louzada Generator (RL-G) with some relevant statistical properties

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Bottiglione, Benedetta, Alessandra Villani, Linda Mastropasqua, Silvana De Leonardis, and Costantino Paciolla. "Blue and Red LED Lights Differently Affect Growth Responses and Biochemical Parameters in Lentil (Lens culinaris)." Biology 13, no. 1 (2023): 12. http://dx.doi.org/10.3390/biology13010012.

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Light-emitting diodes are an attractive tool for improving the yield and quality of plant products. This study investigated the effect of different light intensity and spectral composition on the growth, bioactive compound content, and antioxidant metabolism of lentil (Lens culinaris Medik.) seedlings after 3 and 5 days of LED treatment. Two monochromatic light quality × three light intensity treatments were tested: red light (RL) and blue light (BL) at photosynthetic photon flux density (PPFD) of 100, 300, and 500 μmol m−2 s−1. Both light quality and intensity did not affect germination. At b

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Gai, Sibo, Shangke Lyu, Hongyin Zhang, and Donglin Wang. "Continual Reinforcement Learning for Quadruped Robot Locomotion." Entropy 26, no. 1 (2024): 93. http://dx.doi.org/10.3390/e26010093.

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The ability to learn continuously is crucial for a robot to achieve a high level of intelligence and autonomy. In this paper, we consider continual reinforcement learning (RL) for quadruped robots, which includes the ability to continuously learn sub-sequential tasks (plasticity) and maintain performance on previous tasks (stability). The policy obtained by the proposed method enables robots to learn multiple tasks sequentially, while overcoming both catastrophic forgetting and loss of plasticity. At the same time, it achieves the above goals with as little modification to the original RL lear

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Kokel, Harsha, Junkyu Lee, Michael Katz, Shirin Sohrabi, and Kavitha Srinivas. "How to Reduce Action Space for Planning Domains? (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12989–90. http://dx.doi.org/10.1609/aaai.v36i11.21631.

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While AI planning and Reinforcement Learning (RL) solve sequential decision-making problems, they are based on different formalisms, which leads to a significant difference in their action spaces. When solving planning problems using RL algorithms, we have observed that a naive translation of the planning action space incurs severe degradation in sample complexity. In practice, those action spaces are often engineered manually in a domain-specific manner. In this abstract, we present a method that reduces the parameters of operators in AI planning domains by introducing a parameter seed set pr

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