Relevant bibliographies by topics / RL parameters

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'RL parameters'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "RL parameters"

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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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Dissertations / Theses on the topic "RL parameters"

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Ledbetter, Moira Ruth. "Development of an analytical method to derive hydrophobicity parameters for use as descriptors for the prediction of the environmental and human health risk of chemicals." Thesis, Liverpool John Moores University, 2012. http://researchonline.ljmu.ac.uk/6107/.

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There is a requirement to assess the safety of chemicals to both 'man' and the environment. Traditionally this was determined through the use of animal testing. However, there is an increased need to develop alternatives to animal testing for the determination of toxicity due to ethical and legislative reasons. One approach to replacing the use of animals is the application of computational methods. These include Quantitative Structure-Activity Relationships ((Q)SARs), which are the formalisation of the relationship of the effects (e.g. toxicity) for a series of chemicals and their physico-che
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Main-Knorn, Magdalena. "Monitoring of forest cover change and modeling biophysical forest parameters in the Western Carpathians." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2012. http://dx.doi.org/10.18452/16562.

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Die Umweltveränderungen durch den Menschen sind auf unserer Erde allgegenwärtig. Entwaldung und Waldschädigung beeinflussen das System Erde entscheidend, denn Wälder bieten wichtige Ökosystemleistungen und sind Kernelement der Debatte um den Klimawandel, speziell hinsichtlich der globalen Kohlenstoffbilanz. Veränderungen der Waldbedeckung zu quantifizieren ist daher von herausragendem wissenschaftlichen Interesse. Ziel dieser Arbeit ist es, Waldbedeckungsveränderungen in den Westlichen Karpaten grenzübergreifend zu bestimmen, sowie Dynamiken der Biomasse von Nadelwäldern und deren Auswirkungen
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Tomešová, Tereza. "Autonomní jednokanálový deinterleaving." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445470.

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This thesis deals with an autonomous single-channel deinterleaving. An autonomous single-channel deinterleaving is a separation of the received sequence of impulses from more than one emitter to sequences of impulses from one emitter without a human assistance. Methods used for deinterleaving could be divided into single-parameter and multiple-parameter methods according to the number of parameters used for separation. This thesis primarily deals with multi-parameter methods. As appropriate methods for an autonomous single-channel deinterleaving DBSCAN and variational bayes methods were chosen
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Chapariha, Mehrdad. "Modeling alternating current rotating electrical machines using constant-parameter RL-branch interfacing circuits." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45565.

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Transient simulation programs are used extensively for modeling and simulation of various electrical power and energy systems that include rotating alternating current machines as generators and motors. In simulation programs, traditionally, the machine models are expressed in qd-coordinates (rotational reference frame) and transformed variables, and the power networks are modeled in abc-phase coordinates (physical variables), which represents an interfacing problem. It has been shown in the literature that the method of interfacing machine models and the electric network models plays an impor
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Book chapters on the topic "RL parameters"

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Metelli, Alberto Maria. "Configurable Environments in Reinforcement Learning: An Overview." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_9.

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AbstractReinforcement Learning (RL) has emerged as an effective approach to address a variety of complex control tasks. In a typical RL problem, an agent interacts with the environment by perceiving observations and performing actions, with the ultimate goal of maximizing the cumulative reward. In the traditional formulation, the environment is assumed to be a fixed entity that cannot be externally controlled. However, there exist several real-world scenarios in which the environment offers the opportunity to configure some of its parameters, with diverse effects on the agent’s learning proces
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Zhang, Changjian, Parv Kapoor, Rômulo Meira-Góes, et al. "Tolerance of Reinforcement Learning Controllers Against Deviations in Cyber Physical Systems." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71177-0_17.

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AbstractCyber-physical systems (CPS) with reinforcement learning (RL)-based controllers are increasingly being deployed in complex physical environments such as autonomous vehicles, the Internet-of-Things (IoT), and smart cities. An important property of a CPS is tolerance; i.e., its ability to function safely under possible disturbances and uncertainties in the actual operation. In this paper, we introduce a new, expressive notion of tolerance that describes how well a controller is capable of satisfying a desired system requirement, specified using Signal Temporal Logic (STL), under possible
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Saeed, Muhammad, Hassaan Muhammad, Narmeen Sabah, et al. "Reinforcement Learning to Improve Finite Element Simulations for Shaft and Hub Connections." In ARENA2036. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88831-1_26.

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Abstract Advancements in technology and numerical methods have shifted from slow, resource-intensive software to faster predictive solutions powered by artificial intelligence (AI). An exemplary case is the analysis of interference fit connections between a cylindrical shaft and hub, which has the potential to redefine optimal design, minimizing stress and maximizing torque transmission. Traditional experimental analysis using Finite Element Method (FEM) simulations is undeniably time-consuming, inefficient, and complex, thus necessitating the deployment of AI as a pivotal tool in industrial a
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Lenka, Lalu Prasad, and Mélanie Bouroche. "Safe Lane-Changing in CAVs Using External Safety Supervisors: A Review." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_41.

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AbstractConnected autonomous vehicles (CAVs) can exploit information received from other vehicles in addition to their sensor information to make decisions. For this reason, their deployment is expected to improve traffic safety and efficiency. Safe lane-changing is a significant challenge for CAVs, particularly in mixed traffic, i.e. with human-driven vehicles (HDVs) on the road, as the set of vehicles around them varies very quickly, and they can only communicate with a fraction of them. Many approaches have been proposed, with most recent work adopting a multi-agent reinforcement learning (
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Paranjape, Akshay, Nils Plettenberg, Markus Ohlenforst, and Robert H. Schmitt. "Reinforcement Learning for Quality-Oriented Production Process Parameter Optimization Based on Predictive Models." In Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde230059.

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Production of low-quality or faulty products is costly for manufacturing companies since it wastes a lot of resources, human effort, and time. Avoiding such waste requires the correct set of process control parameters, which depends on the dynamic situation in the production processes. Research so far mainly focused on optimizing specific processes using traditional optimization algorithms, mainly evolutionary algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this paper explores the application of reinforceme
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Mowbray, Max, Ehecatl Antonio Del Rio Chanona, and Dongda Zhang. "Constructing Time-varying and History-dependent Kinetic Models Via Reinforcement Learning." In Machine Learning and Hybrid Modelling for Reaction Engineering. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781837670178-00247.

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In Chapter 8, we demonstrated how to identify an accurate lumped kinetic model structure through reaction network reduction. However, this problem can become challenging if the kinetic parameters are time-varying due to continuous changes of catalyst and enzyme reactivity. Using machine learning methods, in Chapters 3 and 9, we have demonstrated that hybrid modelling provides an effective solution to account for the time-varying nature of kinetic parameters, reducing the model uncertainty. However, another longstanding challenge for predictive modelling of complex chemical and biochemical reac
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Messaoud, Seifeddine, Soulef Bouaafia, Abbas Bradai, Mohamed Ali Hajjaji, Abdellatif Mtibaa, and Mohamed Atri. "Network Slicing for Industrial IoT and Industrial Wireless Sensor Network: Deep Federated Learning Approach and Its Implementation Challenges." In Emerging Trends in Wireless Sensor Networks [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.102472.

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5G networks are envisioned to support heterogeneous Industrial IoT (IIoT) and Industrial Wireless Sensor Network (IWSN) applications with a multitude Quality of Service (QoS) requirements. Network slicing is being recognized as a beacon technology that enables multi-service IIoT networks. Motivated by the growing computational capacity of the IIoT and the challenges of meeting QoS, federated reinforcement learning (RL) has become a propitious technique that gives out data collection and computation tasks to distributed network agents. This chapter discuss the new federated learning paradigm an
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Wang, Di. "Reinforcement Learning for Combinatorial Optimization." In Encyclopedia of Data Science and Machine Learning. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9220-5.ch170.

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Combinatorial optimization (CO) problems have many important application domains, including social networks, manufacturing, and transportation. However, as an NP-hard problem, the traditional CO problem-solvers require domain knowledge and hand-crafted heuristics. Facing big data challenges, can we solve these challenging problems with a learning structure within a short time? This article will demonstrate how to solve the combinatorial optimization problems with the deep reinforcement learning (DRL) method. Reinforcement learning (RL) is a subfield of machine learning (ML) that learns the opt
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Jin, Shan. "A Lowpass-Bandpass Diplexer Using Common Lumped-Element Dual-Resonance Resonator." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220531.

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A lowpass-bandpass (LP-BP) diplexer with one lowpass channel (LPC) and one bandpass channel (BPC) is presented. The lumped-element dual-resonance resonator as common resonator is proposed to connect inductors, capacitors and LC resonator to constitute the desired channels. The LPC design is combined with parameters optimization and the lowpass transformation method, and the BPC design can be developed using the classical design theory of coupled-resonator filter. As an example, a 0.9 / 1.8 GHz LP-BP diplexer is designed and fabricated, which exhibits high return loss (RL), low insertion loss (
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Qiao, Zhongjian, Jiafei Lyu, and Xiu Li. "Mind the Model, Not the Agent: The Primacy Bias in Model-Based RL." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240694.

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The primacy bias in model-free reinforcement learning (MFRL), which refers to the agent’s tendency to overfit early data and lose the ability to learn from new data, can significantly decrease the performance of MFRL algorithms. Previous studies have shown that employing simple techniques, such as resetting the agent’s parameters, can substantially alleviate the primacy bias in MFRL. However, the primacy bias in model-based reinforcement learning (MBRL) remains unexplored. In this work, we focus on investigating the primacy bias in MBRL. We begin by observing that resetting the agent’s paramet
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Conference papers on the topic "RL parameters"

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Olayiwola, Teslim, Kyle Territo, and Jose Romagnoli. "Physics-informed Data-driven control of Electrochemical Separation Processes." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.163984.

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Optimizing the operational conditions of electrochemical separation systems to achieve higher separation efficiency remains a complex challenge due to their nonlinear and dynamic nature. In this work, we proposed a Reinforcement Learning (RL)-based control framework to address this challenge. By applying various RL algorithms, we trained an RL-based controller that adapts to different system configurations and conditions. Also, the trained model learns the optimality between the removal efficiency and energy consumption. Overall, this approach autonomously learns the optimal operational parame
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Lossa, G., O. Deblecker, and Z. De Grève. "Use of an Inference Technique for Sensitivity Analysis of RL Parameters of Wound Inductors." In 2025 International Applied Computational Electromagnetics Society Symposium (ACES). IEEE, 2025. https://doi.org/10.23919/aces66556.2025.11052485.

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Territo, Kyle, Peter Vallet, and Jose Romagnoli. "Towards Self-Tuning PID Controllers: A Data-Driven, Reinforcement Learning Approach for Industrial Automation." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.132857.

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As industries embrace the digitalization of Industry 4.0, the abundance of process data creates new opportunities to optimize industrial control systems. Traditional Proportional-Integral-Derivative (PID) controllers often require manual tuning to address changing conditions. This paper introduces an automated, adaptive PID tuning method using historical data and machine learning for a continuously evolving, data-driven approach. The method centers on training a surrogate model using historical process data to replicate real system behavior under various conditions. This enables safe explorati
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John, Werner, Emre Ecik, Philip Varghese Modayil, et al. "AI-based Hybrid Approach (RL/GA) used for Calculating the Characteristic Parameters of a Single Surface Microstrip Transmission Line." In 2025 Asia-Pacific International Symposium and Exhibition on Electromagnetic Compatibility (APEMC). IEEE, 2025. https://doi.org/10.1109/apemc62958.2025.11051725.

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Slager, N., and M. B. Franke. "Reinforcement learning for distillation process synthesis using transformer blocks." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.115663.

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A reinforcement learning framework is developed for the synthesis of distillation trains. The rigorous Naphtali-Sandholm algorithm for equilibrium separation modeling was implemented in JAX and coupled with the benchmarking Jumanji RL library. The vanilla actor-critic agent was successfully trained to build distillation trains for a seven-component hydrocarbon mixture. A transformer encoder structure was used to apply self-attention over the agent�s observation. The agent was trained on minimal data representation containing quantitative component flows and relative volatility parameters betwe
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Huang, Xiaoge, Ziang Zhang, Shufan Wang, and Jian Li. "Transient Stability Enhancement via a Scalable RL Method with VSG Parameter Tuning." In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2024. https://doi.org/10.1109/iecon55916.2024.10905298.

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Kotecha, Niki, Max Bloor, Calvin Tsay, and Antonio del Rio Chanona. "MORL4PC: Multi-Objective Reinforcement Learning for Process Control." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.161830.

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In chemical process control, decision-making often involves balancing multiple conflicting objectives, such as maximizing production, minimizing energy consumption, and ensuring process safety. Traditional approaches for multi-objective optimization, such as linear programming and evolutionary algorithms, have proven effective but struggle to adapt in real-time to the dynamic and nonlinear nature of chemical processes. In this paper, we propose a framework that combines Reinforcement Learning (RL) with Multi-Objective Evolutionary Algorithms (MOEAs) to address these challenges. Specifically, w
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Kadlecova, Eva, Radek Kubasek, and Edita Kolarova. "RL Circuits Modeling With Noisy Parameters." In 2006 International Conference on Applied Electronics. IEEE, 2006. http://dx.doi.org/10.1109/ae.2006.4382969.

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Dworschak, Fabian, Christopher Sauer, Benjamin Schleich, and Sandro Wartzack. "Reinforcement Learning As an Alternative for Parameter Prediction In Design for Sheet Bulk Metal Forming." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-89073.

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Abstract This contribution presents an approach and a case study to compare Reinforcement Learning (RL) and Genetic Algorithms (GA) for parameter prediction in Sheet Bulk Metal Forming (SBMF). Machine Learning (ML) and Multi-Objective Optimization (MOO) to provide different points of view for the prediction of manufacturing parameters. While supervised learning depends on sufficient training data, GA lack the ability to explain how sufficient parameters were achieved. RL could help to overcome both issues, as it is independent from training data and can be used to learn a policy leading toward
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Huang, Xu, Trieu Phat Luu, Ted Furlong, and John Bomidi. "Deep Reinforcement Learning for Automatic Drilling Optimization Using an Integrated Reward Function." In IADC/SPE International Drilling Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/217733-ms.

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Abstract Drilling optimization is a complicated multi-objective processing optimization problem. During drilling, drillers need to adjust WOB and RPM continuously in a timely manner, not only to maximize ROP, but also to prevent severe vibration and maintain downhole tool durability. In this study, a virtual drilling agent using a deep reinforcement learning (RL) model is developed and trained to automatically make drilling decisions and proven to effectively optimize drilling parameters. A deep RL model using a deep deterministic policy gradient (DDPG) algorithm is developed to optimize drill
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