AIMC Topic: Reinforcement Machine Learning

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ReLMM: Reinforcement Learning Optimizes Feature Selection in Modeling Materials.

Journal of chemical information and modeling Dec 17, 2024
A challenge to materials discovery is the identification of the physical features that are most correlated to a given target material property without redundancy. Such variables necessarily comprise the optimal search domain in subsequent material de...
Models, Chemical Reinforcement Machine Learning
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A fully value distributional deep reinforcement learning framework for multi-agent cooperation.

Neural networks : the official journal of the International Neural Network Society Dec 14, 2024
Distributional Reinforcement Learning (RL) extends beyond estimating the expected value of future returns by modeling its entire distribution, offering greater expressiveness and capturing deeper insights of the value function. To leverage this advan...
Reinforcement, Psychology Reinforcement Machine Learning Algorithms Humans Neural Networks, Computer Deep Learning Cooperative Behavior
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Robotic navigation with deep reinforcement learning in transthoracic echocardiography.

International journal of computer assisted radiology and surgery Sep 20, 2024
PURPOSE: The search for heart components in robotic transthoracic echocardiography is a time-consuming process. This paper proposes an optimized robotic navigation system for heart components using deep reinforcement learning to achieve an efficient ...
Echocardiography Surgery, Computer-Assisted Reinforcement Machine Learning Humans Deep Learning Mitral Valve Algorithms Robotic Surgical Procedures
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Autonomous countertraction for secure field of view in laparoscopic surgery using deep reinforcement learning.

International journal of computer assisted radiology and surgery Sep 16, 2024
PURPOSE: Countertraction is a vital technique in laparoscopic surgery, stretching the tissue surface for incision and dissection. Due to the technical challenges and frequency of countertraction, autonomous countertraction has the potential to signif...
Laparoscopy Phantoms, Imaging Humans Reinforcement Machine Learning Computer Simulation Neural Networks, Computer Deep Learning
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Deep reinforcement learning for Type 1 Diabetes: Dual PPO controller for personalized insulin management.

Computers in biology and medicine Jun 1, 2025
BACKGROUND: Managing blood glucose levels in Type 1 Diabetes Mellitus (T1DM) is essential to prevent complications. Traditional insulin delivery methods often require significant patient involvement, limiting automation. Reinforcement Learning (RL)-b...
Computer Simulation Precision Medicine Adult Male Female Reinforcement Machine Learning Insulin Infusion Systems Humans Deep Learning Insulin Pancreas, Artificial Diabetes Mellitus, Type 1 Blood Glucose Hypoglycemic Agents
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A Deep Reinforcement Learning Based Region-Specific Beamformer for Sparse Arrays 3-D Ultrasound Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Jun 1, 2025
Sparse arrays offer several advantages over other element reduction techniques for 3-D ultrasound imaging. However, the large interelement spacing in these arrays results in high sidelobe-related artifacts, which significantly degrade image quality a...
Humans Algorithms Artifacts Phantoms, Imaging Reinforcement Machine Learning Ultrasonography Imaging, Three-Dimensional Deep Learning
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Optimal Vasopressin Initiation in Septic Shock: The OVISS Reinforcement Learning Study.

JAMA May 20, 2025
IMPORTANCE: Norepinephrine is the first-line vasopressor for patients with septic shock. When and whether a second agent, such as vasopressin, should be added is unknown.
Practice Guidelines as Topic Organ Dysfunction Scores Vasopressins Vasoconstrictor Agents Datasets as Topic Reinforcement Machine Learning Middle Aged Hospital Mortality Observational Studies as Topic Norepinephrine Shock, Septic Female Aged Male Electronic Health Records Humans Clinical Decision-Making Critical Illness
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Enhanced intelligent train operation algorithms for metro train based on expert system and deep reinforcement learning.

PloS one Jan 1, 2025
In recent decades, automatic train operation (ATO) systems have been gradually adopted by many metro systems, primarily due to their cost-effectiveness and practicality. However, a critical examination reveals computational constraints, adaptability ...
Algorithms Deep Learning Expert Systems Humans Railroads Reinforcement, Psychology Reinforcement Machine Learning
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Better Blood Pressure Control for Stroke Patients in the ICU: A Deep Reinforcement Learning with Supervised Guidance Approach for Adaptive Infusion Rate Tuning.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
Blood pressure variability (BPV) plays a critical role in vascular diseases, particularly in acute ischemic stroke patients in intensive care units (ICUs), where higher BPV correlates with increased mortality rates. Current interventions lack effecti...
Deep Learning Nicardipine Hypertension Stroke Blood Pressure Reinforcement Machine Learning Antihypertensive Agents Infusions, Intravenous Humans Intensive Care Units
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