reinforcement learning

Artificial Intelligence supported ventilator management: Current applications, clinical evidence, and future directions

Artificial intelligence-supported ventilator management is a rapidly evolving field with potential to enhance individualized respiratory care and future studies should prioritize external validation, prospective trials, workflow integration, explainability, and safety frameworks to determine whether AI can improve clin...

Supplementary material from "Code-driven planning in grid worlds with large language models"

We propose an iterative programmatic planning (IPP) framework that solves grid-based tasks by using large language models (LLMs) to synthesize interpretable, executable programs that act as agent policies mapping environment states to action sequences, rather than relying on traditional search or reinforcement learning...

Distributed learning-enabled federated large language model for fake news detection

Fake news detection is the process of detecting misinformative or crafted content spread through social media. Fake news causes social instabilities, increases the data leakage risks, and affects the user's privacy. The prevailing fake news detection models failed to tackle certain challenges, including overfitting iss...

Young minds in distress: Exploring the global rise in youth mental health diagnoses.

This review synthesizes global epidemiological trends, differentiates true increases in morbidity from diagnostic inflation, and integrates evidence-based intervention strategies, suggesting a three-tiered intervention framework.

CoVarNet: Contrastive variational network for communication in multi-agent reinforcement learning

Transdiagnostic latent factors dissociating depression and anxiety through reinforcement learning

The comorbidity of depression and anxiety has long been recognized. While mainly characterized as mood dysregulation, depression and anxiety symptoms are also manifested in learning and decision-making deficits. However, the specific cognitive mechanisms that are common to both disorders, as well as those that distingu...

Large Language Model-Enhanced Multi-Agent Reinforcement Learning for Autonomous Enterprise Workflow Orchestration via Graph Semantic Routing

# LeMARLA **Large Language Model-Enhanced Multi-Agent Reinforcement Learning for Autonomous Enterprise Workflow Orchestration via Graph Semantic Routing.** LeMARLA couples an LLM-driven task decomposition module, a heterogeneous graph attention routing module over tasks, employees and skills, and a CTDE multi-agent rei...

Feature-Level Fusion of RGB Camera and 2-D LiDAR for Local Occupancy Estimation and Reactive Navigation: A Controlled Simulation Study

This preprint presents a controlled, systems-level simulation study of feature-level fusion of RGB camera and 2-D LiDAR for local occupancy estimation and reactive navigation of an indoor mobile robot. The system integrates ROS 2, PyTorch and Gazebo in a modular perception-to-navigation pipeline. Three perception confi...

Hierarchical reinforcement learning for load restoration of distribution networks under typhoon disasters

Feature-Level Fusion of RGB Camera and 2-D LiDAR for Local Occupancy Estimation and Reactive Navigation: A Controlled Simulation Study

This preprint presents a controlled, systems-level simulation study of feature-level fusion of RGB camera and 2-D LiDAR for local occupancy estimation and reactive navigation of an indoor mobile robot. The system integrates ROS 2, PyTorch and Gazebo in a modular perception-to-navigation pipeline. Three perception confi...

A hierarchical reinforcement learning and predictive control framework for automated multi-variable anesthesia

BACKGROUND AND OBJECTIVE: Automated multi-variable anesthesia must balance conflicting objectives amid patient variability, safety constraints, and partial observability. The state-of-the-art robust approach uses a Genetic Algorithm (GA) to optimize Generalized Predictive Control (GPC) parameters across a population, b...

Deep reinforcement learning for simulated autonomous heat stress mitigation in dairy cattle

Heat stress significantly constrains dairy cattle productivity and welfare, compromising milk production and fertility. Yet, despite advances in heat stress detection and prediction, conventional mitigation strategies still rely on fixed thresholds, overlooking inter-animal variability and cumulative thermal load. In t...