reinforcement learning

ArchiveGene–UzMorphSim MultiDomain v1.1: An Uzbek Multi-Domain Resource for Semantic Similarity and Document Understanding

ArchiveGene–UzMorphSim MultiDomain v1.1 is a multi-domain Uzbek research dataset developed for semantic similarity, adaptive model routing, document understanding, layout-aware processing, and document-orientation analysis. The resource comprises a primary corpus of 2,000 text documents for semantic-similarity experime...

A Reliability-Aware Closed-Loop Adaptive Learning Framework for Intelligent Energy Management in Photovoltaic-Powered Electric Vehicle Charging Stations

Introducing Earthquake Preparedness from Early Childhood: The “Earthy and Quaky” Education Program as an Interdisciplinary and Learning Pathway Toward a Culture of Safety

Destructive earthquakes are natural hazards highlighting the importance of introducing disaster preparedness from early childhood. Preschool children require developmentally appropriate educational approaches that connect scientific understanding with experiential, play-based, and participatory learning. This study pre...

Reinforcement Learning Enabled Agentic AI

Intelligent management and resilience assessment of integrated energy system in industrial park based on digital twin and deep reinforcement learning

The integrated energy system (IES) of industrial park is the key carrier to achieve the “double carbon” goal, but its characteristics such as strong coupling of multi-energy flow, high proportion of new energy access and multiple uncertainties pose severe challenges to the intelligent operation and resilience of the sy...

Changes in research competency of public health personnel following an empowerment-based capacity development program in Thailand: a convergent parallel mixed-methods study

Introduction Research competency among frontline public health personnel is a prerequisite for evidence-informed local health systems, yet most capacity-building programs in low- and middle-income settings are short didactic courses that are rarely evaluated with both outcome and process data. This study examined chang...

ColonelAdaptAI a prototype guided perturbation learning framework for stable continual neural network adaptation

ColonelAdaptAI, a continual learning framework based on the Informative Perturbation Network, offers a pragmatic, efficient, and interpretable approach to continual learning for applications ranging from robotics and health care diagnosis to autonomous systems and any field that requires perpetual learning and generali...

MAGE: Matter Autogenerative Engine - Recursive Self-Training Generative Intelligence for Universal Matter Fabrication

MAGE (Matter Autogenerative Engine) is a research framework for investigating whether manufacturing can be transformed from a collection of human-designed processes into an increasingly general learned capability of autonomous physical intelligence. The long-term objective is a physically grounded form of text-to-matte...

Striatal interneuron microcircuits gate reinforcement to stabilize adaptive choice

The dorsomedial striatum guides learning and adaptive decision-making through excitatory synaptic control of its spiny projection neuron outputs. However, the contributions of local inhibitory microcircuitry remain poorly understood. Here, we identify an interneuron circuit in the dorsomedial striatum that links outcom...

SEB-IOTA: a trust-driven and hierarchically verified data broadcasting framework for smart grids with HMM-anchored reputation dynamics

To address secure, low-latency broadcasting of high-value data in smart grids, this paper aims to propose secure and efficient broadcast framework based on IOTA (SEB-IOTA). It tackles three key bottlenecks: single points of failure in centralized architectures, poor real-time performance of conventional blockchain...

Code for: Discovering the Logic of the Environment through Explainable Neuro-Symbolic Reinforcement Learning

This repository contains the Python code, custom Gymnasium environments, and training scripts for the neuro-symbolic reinforcement learning framework described in the manuscript submitted.

Machine learning-driven optimization of circuit design

The rapid progression of electronic systems demands more and more efficient and reliable circuit design. Optimization methods often face the complexity of modern circuits and limitations in controlling the wider design space. This chapter on circuit design explores the integration of machine learning (ML) techniques as...