reinforcement learning

基于多智能体强化学习的复杂系统调度

This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to the complex system scheduling problem. Traditional scheduling methods often struggle to adapt to dynamic and intricate system environments, leading to suboptimal performance. This research proposes a novel framework leveraging MARL...

Protocolo internacional “Resc-IA-Sepsis”: un sistema de aprendizaje por refuerzo para la intervención quirúrgica multidisciplinaria y guiado por biomarcadores en choque séptico y falla multiorgánica

Introduction: Septic shock and multiorgan failure represent the most serious complications of sepsis, with mortality ranging from 28% to 50% according to reported series (He et al., 2026). Clinical decision support systems based on artificial intelligence have emerged as promising tools to optimize the management of th...

An Intelligent Construction Method for Petrochemical Datasets Based on RLHF and Data De-Identification

To mitigate high expert-annotation costs, domain-preference misalignment, and the inherent trade-off between sensitive-data protection and training utility in petrochemical dataset construction, an iterative framework combining human-feedback-aligned reinforcement learning (RLHF) with post hoc data de-identification is...

Simplified reinforcement learning for energy management of extended-range electric vehicles based on STM32

This paper proposes a lightweight, simplified Q-learning energy management strategy for extended-range electric vehicles (REEVs), successfully implemented on an STM32 microcontroller, fully satisfying strict on-board embedded system constraints and providing a highly feasible solution for intelligent REEV energy manage...

Deep reinforcement learning-based optimal energy management for microgrids

Experimental results demonstrate that the proposed DRL-based framework provides an effective and scalable solution for intelligent microgrid energy management and outperforms conventional rule-based strategies and model-based optimization approaches in terms of operational cost reduction, energy utilization efficiency,...

A closed-loop automated control architecture with physics-informed compensation for springback regulation in aerospace tube bending manufacturing

Springback in aerospace tube bending is a closed-loop control problem; the elastic rebound of the workpiece after tool release represents a systematic output error caused by material nonlinearities and batch-to-batch parametric uncertainties through a conventional fragmented workflow that lacks any feedback path for se...

Method of integrating deep reinforcement learning for electricity price prediction and intelligent trading strategy construction in smart grids

An intelligent trading strategy model based on Deep Deterministic Policy Gradient (DDPG) on the basis of the prediction model is constructed, providing optimal trading strategies for both electricity generation and consumption parties in the market.

Explainability-driven LangChain-integrated large language models and knowledge graphs for multiagent reinforcement learning in complex air combat simulation

This work advances multiagent reinforcement learning (MARL) for complex air combat by integrating methods that enhance decision-making and explainability. Using a realistic six-degrees-of-freedom aerial simulation built on the OpenAI Gymnasium framework, we investigate competitive agent interactions in dynamic scenario...

Interference modeling and adaptive suppression algorithm for HPLC and 920MHz wireless fusion communication in complex building environments

A composite interference recognition model based on short-time Fourier transform (STFT) and deep learning and a fast anti-interference algorithm based on transfer reinforcement learning (TrRL) is proposed, combining multi-agent Q-learning and value function transfer mechanism, adaptive spectrum access and power control...

Evaluating Deep Actor–Critic Methods for Path Planning of Mobile Manipulators Under Wheel–Terrain Interaction

The proposed framework incorporates a slip-aware reward formulation that penalizes discrepancies between commanded and measured robot motion while accounting for longitudinal and lateral slip resulting from wheel–terrain interaction, demonstrating the potential of slip-aware RL for coordinated SSMM navigation.

Human-robot collaboration disassembly sequence planning for end-of-life smartphones using Q-learning algorithm

Beyond Hand Skills: A Case Analysis of Empathy and Critical Thinking Instruction

Background In addition to scientific knowledge and technical proficiency, the compassionate care model of oral health care requires clinicians to develop and reliably demonstrate the skills of empathy and critical thinking. The benefits of these skills have been well documented and are accrued by both patients and clin...