reinforcement learning

A reinforcement learning-based spatiotemporal dynamic multi-graph ensemble framework for multi-station air quality prediction

An evolutionary-guided deep reinforcement learning approach to detect influential nodes from networked systems

An automatic parameter search framework for agricultural machinery trajectory operation mode identification by deeply weakly informed deep reinforcement learning

Digital twin-driven deep reinforcement learning for coordinated scheduling and state prediction of distributed energy storage clusters

THE ROLE OF ARTIFICIAL INTELLIGENCE IN DIABETES CARE AND MANAGEMENT: A COMPREHENSIVE REVIEW

Diabetes mellitus represents a global metabolic crisis characterized by complex self-management requirements, marked physiological variability, and significant risks of microvascular and macrovascular complications. The integration of Artificial Intelligence (AI)—including Machine Learning (ML), Deep Learning (DL), Nat...

Reproduction code and screened corpus for a survey of evaluation design in reinforcement learning-based route guidance

Reproduction material for the survey "Study-Area Boundaries in Reinforcement Learning-Based Route Guidance: A Survey of Evaluation Design and ReportingRequirements" by Donghoun Lee. The archive contains the grid, benchmark and SUMO environments, the planner, the training code, the screened corpus withthe recorded field...

AI in energy and water conservation

AI-Enabled National Water Conservation and Security System for Qatar A Research and Policy Framework for an Intelligent National Water Digital Twin, Predictive AI and ILM-Based Water Intelligence Platform Prepared as a concept paper for Qatar Government consideration Abstract Qatar’s water security challenge is fundame...

Current progress on control strategies for underwater soft robots: a comprehensive review

Underwater soft robots (USRs) operate in highly dynamic, unstructured, and sensor-limited environments where fluid-structure interaction, compliance, and nonlinear actuation significantly complicate closed-loop control compared with both rigid underwater robots and terrestrial soft robots. As a result, classical underw...

Editorial: Smart forecasting: deep learning and explainable AI for real-world time series prediction

For analytical clarity, the contributions can be organized into four interrelated thematic areas: Smart Forecasting Beyond Finance; Deep Learning and Hybrid Models for Financial Forecasting; Large Language Models for Next-Generation Forecasting; and Perspectives on Intelligent Forecasting. Together, these themes reflec...

Semantic Memory-Regularized UAMetaRL for Adaptive Task Scheduling in 6G Edge Intelligence

This repository provides the source code, configuration files, synthetic datasets, and experimental results for the paper entitled "Semantic Memory-Regularized Uncertainty-Aware Meta-Reinforcement Learning for Adaptive Task Scheduling in 6G Edge Intelligence". The repository implements a semantic memory-regularized unc...

KODEX — KDRIVE: RL-control

KODEX — KDRIVE (RL-control). reinforcement-learning controller — a feedback policy learned by the cross-entropy method in the twin plant loop; clamped by KGATEBenchmark: RL policy (cross-entropy): tracking **0.0551** vs naive 0.1225, twin-in-the-loopPart of KODEX, the Kronos Family of Codes — a benchmarked AI/ML fusion...

Digital Supply‐Chain Talent Development

In this chapter, the author provides an overview of the guiding principles of machine learning (ML), representation, optimization, and generalization, in the well-designed data pipelines. It reviews the bias-variance trade-off as a theoretical model that helps gauge the development of the model and how they perform. It...