reinforcement learning

Physics-constrained multi-agent reinforcement learning within a cognitive digital twin for scalable and cyber-resilient distribution network reconfiguration

A review of full-stack autonomous obstacle avoidance for assistive robots for the disabled

With the intensification of population aging and the increasing awareness of protecting the rights of the disabled, significant progress has been made in assistive robots for visually impaired people, the mobility impairments and other groups. Research in this field focuses on four key technical dimensions: environment...

A framework for benchmarking traffic signal control robustness under incidents: comparative study of reinforcement learning-based methods

T-REX is introduced, an open-source, SUMO-based simulation framework for training and evaluating RL-TSC methods under dynamic, incident scenarios, and shows that while independent value-based and decentralized pressure-based methods offer fast convergence and generalization in stable traffic conditions and homogeneous...

Selective laser melting of LM25 aluminium alloy reinforced with boron carbide and graphite for machine learning based wear prediction and microstructural characterization

The Selective Laser Melting (SLM) is a modern and innovative aluminium alloy metals additive synthesis technique for fabricating lightweight aluminium matrix composites with complex geometries and superior engineering performance. In the present investigation, LM25 aluminium alloy reinforced with boron carbide (B...

Variance‐Guided Evolutionary Reinforcement Learning for Multi‐Objective Energy Scheduling in Dairy Farms

Electricity consumption on dairy farms has a direct impact on operational cost, renewable energy utilisation and load flexibility. Coordinating electric water heating and battery storage under dynamic tariffs, photovoltaic generation and temperature constraints forms a challenging multi‐objective sequential decision‐...

Lessening the shock of defibrillation with machine learning

A reinforcement learning agent trained on numerical simulations of cardiac arrhythmia reduced the amplitude of pulses used to treat cardiac arrhythmias.

Reinforcement Learning With a Low‐Cost Parallel Linkage Gripper for Static and Dynamic Planar In‐Hand Manipulation

Parallel linkage grippers provide a low‐cost and compact alternative to anthropomorphic hands for planar manipulation tasks. However, their closed‐chain kinematics introduce actuator coupling, singularity‐related constraints, and position‐dependent manipulation capability, making closed‐loop control challenging. This w...

Research on Multi-Objective Task Scheduling Optimization and Reinforcement Learning Decision-Making Methods for Edge-Cloud Collaborative Scalable Information Systems

INTRODUCTION: Edge-cloud schedulers must coordinate latency, energy, load balance, and deadline compliance under changing demand while keeping task-arrival and throughput units physically consistent.OBJECTIVE: This study evaluates MORL-ECSO under an auditable, paired-seed simulation protocol and compares it with tuned...

Physics-Informed Deep Reinforcement Learning with Adaptive Action Masking for Secure Second-Level Emergency Scheduling in Energy Networks

With the integration of a high proportion of volatile renewable energy into modern power grids, systems frequently face severe supply-demand imbalances and escalated situational risks under extreme events. Addressing the high-dimensional risks and stringent response timing requirements in real-time markets of new-type...

Implementation of the Iqra’ Method to Improve Qur’an Reading Skills at Rahmah Al-Asri Qur’anic Education Center, Unit 267, Basirih Selatan Subdistrict, Banjarmasin

This study aims to analyze the implementation of the Iqra’ method in improving Qur’anic reading skills among students at TPA Rahmah Al-Asri Unit 267, Basirih Selatan, Banjarmasin, and to examine the supporting and inhibiting factors influencing its implementation. This study employed a qualitative field research approa...

Deep Reinforcement Learning-Based Coordinated Voltage Control of HST and DG

Findings indicate that deep reinforcement learning provides an effective optimization approach for coordinated HST and DG control and offers a useful reference for voltage regulation in active distribution networks.

THE IMPACT OF DIGITAL TRANSFORMATION ON ORGANIZATIONAL RESILIENCE: EVIDENCE FROM SMES IN VIETNAM

The findings reveal that digital transformation positively and directly enhances organizational resilience, and a complementary relationship between learning and innovation is uncovered, highlighting their mutual reinforcement in strengthening firms’ ability to withstand disruption.