reinforcement learning

Second-order policy gradient methods for the linear quadratic regulator

Policy gradient methods are a powerful family of reinforcement learning algorithms for continuous control that optimize a policy directly. However, standard first-order methods often converge slowly. Second-order methods can accelerate learning by using curvature information, but they are typically expensive to compute...

Innovation in Energy Transition: Lessons from Digital Twin and Industry 4.0 Deployments

Digital twin (DT) technologies, tightly coupled with Industry 4.0 methodologies, are emerging as fundamental enablers for innovation and optimization in renewable and low-carbon energy systems. Unlike traditional offline simulation, DTs support a live, bidirectional connection to physical assets and networks, enablin...

Strategies of Al-Qur’an and Hadith Teachers in Improving Students’ Memorization of Short Surah at Madrasah Ibtidaiyah An Najah Haur Kuning

This study aims to analyze the strategies employed by Al-Qur’an and Hadith teachers to improve students’ memorization of short surah and to identify the factors that hinder their implementation at Madrasah Ibtidaiyah An Najah Haur Kuning, Banjar Regency, South Kalimantan. This study employed a qualitative approach with...

Performance E valuation of E xploration S trategies on L earning E fficiency and S tability in Q-Learning

A fundamental problem in reinforcement learning is the trade-off between exploration and exploitation. The quality of the final policy is not the only factor that varies with different exploration strategies, so do learning speed and result stability. Based on Q-learning, this study compares three exploration strategie...

AI-Driven Calibration Procedure Generation for Field Instruments Using Historical Logs

Abstract Calibration of field instruments—such as pressure transmitters, flow meters, temperature sensors, and vibration sensors—is essential for accurate measurements and safe industrial operations. In practice, calibration steps are embedded within governed method statements for assets like PLC/BMS panels, BTU meters...

Intelligent Control Strategy for Automotive Regenerative Braking Based on Deep Learning

A two-layer hierarchical control setup based on deep reinforcement learning and its main elements is looked at, and an organized forecast for the future of the field is offered, with special attention to four possible developments: multi-objective coordinated optimization, transfer learning, distributed vehicle-road co...

A Survey on Resource Scheduling and Optimization for Space‑Air‑Ground Integrated Communication‑Sensing‑Computation Networks

The development of sixth generation (6G) communications is aimed at achieving global connectivity, covering oceans and remote areas with limited ground infrastructure. With the integration of sensing, communication and computing (ISCC) technology, the space-air-ground integrated network (SAGIN) is evolving from a simpl...

Intelligent Underwater Quantum Communications: A Review of AI-Enabled Channel Estimation, Noise Mitigation, and System Optimization

Underwater quantum communication (UQC) has emerged as a promising approach for secure information transfer in oceanic environments by exploiting fundamental quantum mechanical principles. However, realizing UQC in practice remains highly challenging due to the inherently dynamic nature of the underwater optical channel...

Smart Self-Healing AI-Driven Blowout Preventer

Abstract Objectives/Scope Blowout Preventers (BOPs) are the final and most critical line of defense in maintaining well control, yet conventional elastomeric sealing elements experience rapid degradation when exposed to HPHT environments, corrosive fluids, and abrasive cuttings. This degradation increases the probabili...

Reinforcement learning-based adaptive coordination of solar energy for load, battery, electric vehicle, and hydrogen storage

Multi-stage representative view selection and hard-view learning for three-dimensional model classification

Toward brain-trained humanoid robots

A growing corpus of experimental research demonstrates that neuroimaging data can be leveraged to improve the performance of artificial intelligence models, including reinforcement learning models in robotics. However, most existing studies have focused on vision, audition, language, or reward and error signals recorde...