reinforcement learning

eta-ctrl

A modular framework for rolling-horizon operational control using a Gym-compatible agent–environment abstraction. The framework enables integration of optimization-based agents (Pyomo), reinforcement learning agents (Stable-Baselines3), heuristic search methods, and rule-based controllers. Environments can be instantia...

PPORLD-EDNetLDCT: A Proximal Policy Optimization-based reinforcement learning framework for adaptive low-dose CT denoising

Low-dose computed tomography (LDCT) is critical for minimizing radiation exposure, but it often leads to increased noise and reduced image quality. Traditional denoising methods, such as iterative optimization or supervised learning, often fail to preserve image quality. To address these challenges, we introduce PPORLD...

Artificial Intelligence in Financial Forecasting: Accuracy and Limitations

This study critically investigates the evolving role of artificial intelligence (AI) in financial forecasting through a systematic literature review conducted across multiple reputable academic databases, and identifies persistent limitations, including model opacity, data quality concerns, and compliance challenges.

Surrogate-Assisted Deep Reinforcement Learning for Bi-Level Multi-Objective Capacity Sizing of Railway Collaborative Power Supply System

Artificial Intelligence and Machine Learning for Business Management

Artificial Intelligence and Machine Learning for Business Management is an academic textbook that presents an integrated understanding of artificial intelligence (AI), machine learning (ML), business analytics, managerial decision-making, strategy, digital transformation, responsible AI, governance, sustainability, and...

Effects of relational graph modularity and depth on the learning performance of neural networks

In recent years, graph-based machine learning techniques, such as reinforcement learning and graph neural networks, have garnered significant attention. While some recent studies have started to explore the relationship between the graph structure of neural networks and their predictive performance, they often limit th...

Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search

This work proposes a framework with two complementary components: a permutation-invariant hierarchical module that captures interactions across features, operations, and abstraction levels, with a self-attention pooling mechanism that maps semantically equivalent structures to consistent embeddings aligned with downstr...

HiRAD: A Flexible Large-Scale AGV Routing System

HiRAD is proposed, a hierarchical RL framework for continuous-space AGV routing with real-time guarantees that reduces makespan by 45 percent to 63 percent and shortens end-to-end runtime.

Reinforcement learning-based intelligent control algorithm for energy-efficient and emission-aware optimization in industrial power systems

This paper proposes a novel Reinforcement Learning–Based Intelligent Control (RL-IC) algorithm for energy-efficient and emission-aware optimization in industrial power systems. Unlike conventional rule-based or model-predictive controllers that rely on fixed dynamics, RL-IC employs a hierarchical dual-objective learnin...

Teachers’ Experiences in Building Motivating and Anxiety-Reducing Language Learning

Adaptive E2E Monitoring Path Selection with Deep Reinforcement Learning in Multi-domain Optical Networks

Supplemental Materials for “Planning for Leakage-Aware Collaborative Carbon Mitigation: Optimizer-Informed Multi-Agent Reinforcement Learning in Shandong’s Dual Metropolitan Areas”

Supplementary materials data set for the paper tilted “Planning for Leakage-Aware Collaborative Carbon Mitigation: Optimizer-Informed Multi-Agent Reinforcement Learning in Shandong’s Dual Metropolitan Areas”