reinforcement learning

Penguatan Kompetensi Digital Pendidik Nonformal melalui Pelatihan Pengembangan Sumber Belajar Digital Berbasis Learning by Doing

Limited skills among nonformal educators in developing digital technology-based learning resources remain a challenge in improving the quality of learning in equivalency education. This community engagement program aimed to improve nonformal educators’ knowledge and skills in designing, developing, and integrating digi...

SCRAMBLE: Safe Cooperative Risk-Aware Multi-Agent Belief Learning for Embodied Reinforcement Learning in Partially Observable Dynamic Environments

Safe decision-making in partially observable dynamic environments remains a fundamental challenge for multi-agent embodied reinforcement learning, where agents must cooperate with limited local observations while responding to evolving environmental changes and safety-critical interactions. Existing methods often empha...

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...

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