reinforcement learning

Mission-Horizon Hierarchical Reinforcement Learning for Fleet-Level PHM Under Resource-Constrained Operations

Fleet-level prognostics and health management (PHM) requires decisions that extend beyond diagnosis, remaining useful life estimation, and maintenance timing. Mission commitment, aircraft dispatch, maintenance, and spare-parts support consume the same finite sustainment capacity but operate at different temporal scales...

Evaluating the Effectiveness of an AI-Assisted Personalized Learning Pathway in Mobile-Supported Online Physical Education

This study evaluates the effectiveness of an AI-assisted personalized learning pathway in mobile-supported online physical education. To overcome issues of homogeneous content and delayed feedback, a system integrating cognitive knowledge, physical fitness, and motor skills was developed. Reinforcement learning dynamic...

A privacy-preserving task offloading method in collaborative mobile edge and cloud computing based on deep reinforcement learning

(منظومة المختبر الكيميائي الذكي المستقل المأهول بروبوتات شبيهة بالإنسان وأجهزة تفاعلية متعددة الوسائط).

الوصف التقني المموه والمطول للاختراع (Deeply Obfuscated & Extended Technical Description) 1. النطاق الأرشيتكتوري للكيانات الحسابية والتشغيلية الموزعة (Distributed Algorithmic Entity Fabric) تعتمد البنية الهيكلية للاختراع على مصفوفة متجانسة مؤلفة من عشر عقد تشغيلية ذاتية الموائمة الميكانيكية والهندسية، تتميز بتجريد تام...

Priority-driven reinforcement learning for safe control: addressing internal dependent state constraints in non-affine discrete-time systems

This work presents a model-free reinforcement learning methodology for output tracking control of unknown non-affine discrete-time systems with explicit safety guarantees on internal states. The proposed framework employs a fuzzy rule-emulated actor-critic architecture – built on two Multi-Input Fuzzy Rule-Emulated Net...

Behavioral Organization in Embodied AI: A Three-Level Survey

Recent years have witnessed a rapid proliferation of learning, control, and planning algorithms for embodied AI. However, grouping methods by model family or learning paradigm often separates approaches serving the same behavioral function, hindering comparison. To address this gap, this survey classifies robotic manip...

Weakly Supervised Leaf Disease Segmentation via Reinforcement Learning-Based Prompt Optimization for SAM

Accurate segmentation of leaf lesions is essential for crop disease monitoring and precision agriculture. However, most existing methods rely on pixel-level annotations, which are costly and difficult to obtain in real agricultural scenarios. Weakly supervised approaches alleviate this requirement but often suffer from...

Dynamic attention for container relocation problems: a deep reinforcement learning approach

Experimental Research of Hybrid Reinforcement Learning Methods for QoS-Aware Web Service Composition: an Actor-Critic Approach

A high-order discontinuous Galerkin framework combining volume-penalization immersed boundaries and dynamic p-adaptation for moving-body and fluid-structure interaction simulations

This study presents a high-order numerical framework for fluid-structure interaction (FSI) problems involving rigid motion or deformable elastic solids subjected to low Reynolds number viscous flow. The Navier-Stokes equations in the fluid domain are solved using a high-order nodal discontinuous Galerkin (DG) solver HO...

Global State-Guided Reinforcement Learning-Assisted Differential Evolution for Multi-Objective Optimization

Contact-History-Based Velocity-Tracking Reward Derived from Center-of-Mass Controllability for Quadruped Locomotion

Velocity tracking is a fundamental task in reinforcement learning for quadruped locomotion. Conventional velocity-tracking rewards are typically formulated in terms of the squared error between commanded and actual velocities. In this study, we propose a velocity-tracking reward from the controllability Gramian of cent...