reinforcement learning

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR BUSINESS

artificial intelligence (AI) and machine learning (ML) have become general-purpose technologies that are reshaping how organisations make decisions, serve customers, design products and run operations. This chapter provides a management-oriented treatment of the principal AI and ML techniques and of the organisational...

AI-Based Methods for Reliable Building Performance Management

Reliable building performance management involves reducing energy use and peak demand while maintaining indoor comfort and operational safety. Achieving these objectives requires effective strategies across the building life cycle, from early-stage design to operation. However, the availability, quality, and structure...

A chance-constrained and constraint-aware reinforcement learning methodology for hydrogen-integrated energy systems under uncertainty

Geometry-aware deep reinforcement learning for real-time integrated scheduling and nesting in additive manufacturing

Data Set of Concrete Beam–Slab Bridge Superstructures for Machine Learning–Based Estimation of Structural Parameters

The load rating of existing road bridges often requires construction drawings to analytically determine internal forces and member capacities. When such documentation is unavailable, engineers must rely on costly load tests, advanced field surveys, or expert judgment. To support the development of estimation models...

SADRL-paper: code and trade logs for "A Spread-Aware Deep Reinforcement Learning Framework Using Directional Changes Sampling for High Frequency FX Trading"

Code accompanying A Spread-Aware Deep Reinforcement Learning Framework Using Directional Changes Sampling for High Frequency FX Trading (G. Rayment, T. Papastylianou, M. Kampouridis), revised for Array. Attached: per-trade logs behind the paper's results. Every completed trade of every evaluation run, with executed ent...

Deep reinforcement learning for autonomous driving decision-making: Algorithms, applications and challenges

Autonomous driving has the potential to greatly enhance road safety, traffic efficiency, and overall transportation effectiveness. The decision-making module serves as the core intelligence of the system, producing high-level commands for lane keeping, lane changing, and adaptive cruise control. Deep reinforcement lear...

Design of Insulin Pump Control by Reinforcement Learning

  Applying the minimal amount of insulin to effectively control plasma glucose concentrations is important to decrease potential insulin resistance and allow for the effective treatment of diabetes patient for entire lifespan. We are interested in the design of insulin pump infusion controller that is represented by a...

Reviewer #2 (Public review): State-, Trait- and Recovery-Related Differences in Reinforcement Learning and Value-Based Decision Making in Major Depressive Disorder

A Comparative Study of Model Predictive Control and Deep Reinforcement Learning for Distributed Traffic Signal Control

This paper presents a comparative study of two distributed traffic signal control approaches: a cell-transmission-model-based model predictive control (MPC) method and a deep reinforcement learning (DRL) method based on latent spatio-temporal network (LST-Net). In the MPC method, each intersection predicts local traffi...

Digital Twins and Offline Reinforcement Learning for Mechanical Circulatory Support Decisions: What the Current Evidence Can and Cannot Support

Machine Learning-Based Prediction of the Dry Sliding Wear Behaviour of Al2O3-Al6061 Metal Matrix Composites

This study investigated the dry sliding wear behaviour of Al2O3 particulates (2–6 wt.%) in Al6061 metal matrix composites produced by ultrasonic stir casting, examining mechanical and tribological behaviour. We modelled wear rate using machine learning. Optical studies confirmed uniform Al2O3 particle dispersion, minim...