AIMC Topic: Reinforcement Machine Learning

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Leveraging agent-based models and deep reinforcement learning to predict taxis in cell migration.

NPJ systems biology and applications Aug 26, 2025
We present a novel computational framework that combines Agent-Based Modeling (ABM) with Reinforcement Learning (RL) using the Double Deep Q-Network (DDQN) algorithm to determine cellular behavior in response to environmental signals. With this appro...
Humans Algorithms Reinforcement, Psychology Reinforcement Machine Learning Computational Biology Cell Movement Models, Biological Computer Simulation Deep Learning
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An novel cloud task scheduling framework using hierarchical deep reinforcement learning for cloud computing.

PloS one Aug 21, 2025
With the increasing popularity of cloud computing services, their large and dynamic load characteristics have rendered task scheduling an NP-complete problem.To address the problem of large-scale task scheduling in a cloud computing environment, this...
Humans Algorithms Reinforcement Machine Learning Deep Learning Cloud Computing
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Spatio-Temporal SIR Model of Pandemic Spread During Warfare with Optimal Dual-use Health Care System Administration using Deep Reinforcement Learning.

Disaster medicine and public health preparedness Jul 21, 2025
OBJECTIVES: Large-scale crises, including wars and pandemics, have repeatedly shaped human history, and their simultaneous occurrence presents profound challenges to societies. Understanding the dynamics of epidemic spread during warfare is essential...
Models, Theoretical Reinforcement Machine Learning Computer Simulation Warfare Spatio-Temporal Analysis Humans Deep Learning Pandemics
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Shapley value-driven multi-modal deep reinforcement learning for complex decision-making.

Neural networks : the official journal of the International Neural Network Society Jun 21, 2025
Deep Reinforcement Learning (DRL) has made significant strides in addressing various sequential decision-making problems, particularly in domains such as game simulations and robotic control. However, substantial challenges arise when DRL is applied ...
Humans Decision Making Reinforcement, Psychology Reinforcement Machine Learning Neural Networks, Computer Deep Learning
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Deep reinforcement learning control as an innovative approach for urban drainage systems: review and prospects.

Water research Jun 4, 2025
Urban drainage systems (UDSs) are vital for managing stormwater and wastewater but face growing challenges due to urbanization, climate change and aging infrastructure. Real-time control (RTC) enhances UDSs' performance and circumvents the need for s...
Reinforcement Machine Learning Drainage, Sanitary Waste Disposal, Fluid Water Quality Deep Learning Cities Wastewater
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Sign potential-driven multiplicative optimization for robust deep reinforcement learning.

Neural networks : the official journal of the International Neural Network Society Apr 25, 2025
Deep Reinforcement Learning (DRL) has attracted the interest of researchers due to its ability to provide valuable solutions to a variety of problems in different fields, such as robotics, autonomous driving, financial trading, and more. However, DRL...
Reinforcement, Psychology Reinforcement Machine Learning Algorithms Deep Learning Humans Neural Networks, Computer
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Enhancing the resilience of urban drainage system using deep reinforcement learning.

Water research Apr 19, 2025
Real-time control (RTC) is an effective method used in urban drainage systems (UDS) for reducing flooding and combined sewer overflows. Recently, RTC based on Deep Reinforcement Learning (DRL) has been proven to have various advantages compared to tr...
Drainage, Sanitary Reinforcement Machine Learning Rain Deep Learning China Models, Theoretical Floods Cities
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Deep reinforcement learning for decision making of autonomous vehicle in non-lane-based traffic environments.

PloS one Apr 16, 2025
Existing research on decision-making of autonomous vehicles (AVs) has mainly focused on normal road sections, with limited exploration of decision-making in complex traffic environments without lane markings. Taking toll plaza diverging area as an ex...
Decision Making Computer Simulation Reinforcement Machine Learning Automobile Driving Automobiles Deep Learning Humans Algorithms
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Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.

Nature communications Apr 15, 2025
Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To a...
Genetic Predisposition to Disease Computational Biology Markov Chains Risk Factors Deep Learning Carcinoma, Renal Cell Reinforcement Machine Learning Kidney Neoplasms ErbB Receptors Humans
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CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.

Genome biology Apr 7, 2025
Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with real...
Single-Cell Analysis Sequence Analysis, DNA Ovarian Neoplasms Reinforcement Machine Learning DNA Copy Number Variations Breast Neoplasms Deep Learning Humans Algorithms Female
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