reinforcement learning

MutateRL: Reinforcement learning-based test input generation with dynamic mutation selection for deep neural networks

Hierarchical graph reinforcement learning for energy-efficient dual-resource distributed flexible job shop scheduling

Safe and energy-efficient wide-range gas turbine control via condition-adaptive reinforcement learning

Proactive service migration in vehicular edge computing via map-aware mobility prediction and dynamic action-space deep reinforcement learning

Beyond accuracy in crop classification: calibration, selective prediction, and robustness

Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as an agronomic recommendation. Materials and methods: Ten classifiers were evaluated using five matched st...

Data Quality, Feature Engineering, and Model Reliability in Large-Scale AI Multi-Agentic Systems

Large-scale artificial intelligence (AI) systems increasingly operate not as a single monolithic model but as a population of interacting, specialized agents separate models or decision-making components responsible for functions such as pricing, fraud detection, ranking, routing, and customer support that share underl...

Scalable Real-Time Data Pipelines for AI and Machine Learning–Driven Enterprise Systems

The growth of enterprise data volumes across the 2000s and 2010s pushed traditional batch-oriented data processing infrastructures past their practical limits, motivating a sustained shift toward distributed, stream-based architectures capable of supporting real-time analytics and machine learning (ML). This article sy...

A Hybrid Fuzzy–Deep Reinforcement Learning Framework for Real-Time Heart Patient Prioritization in Fog–Cloud Healthcare Systems

Meta reinforcement learning for robust quadruped locomotion under joint failures

Joint failures can disrupt gait coordination, velocity tracking, and body stability in quadruped robots by reducing the available control authority. This study investigates faults in which joints remain locked at fixed angles and presents Adjudicate Reconfiguration and Evolving Regularization Meta Reinforcement Learnin...

Unified Reinforcement-learning-assisted PI Gain Adaptation for CC-CV Control of an SPS-modulated Dual-active-bridge EV Fast Charger

This paper proposes a unified reinforcement-learning-assisted PI controller for CC-CV charging of an SPS dual-active-bridge converter.A DDPG agent acts as a supervisory layer that adapts bounded current-and voltage-loop PI gains while preserving deterministic mode logic, anti-windup, phase saturation, and pulse generat...

A Systematic Review of Artificial Intelligence ( AI )‐Based Task Scheduling Techniques for Cloud‐Fog Computing Environments

ABSTRACT The rapid proliferation of latency‐sensitive and data‐intensive applications has accelerated the adoption of cloud–fog computing architectures to support real‐time processing and distributed resource management. However, efficient task scheduling in cloud–fog environments remains a challenging problem because...

CBGA-SAC: A CVaR-Constrained Deep Reinforcement Learning Framework for Portfolio Optimization