reinforcement learning

Reinforcement Learning vs. MILP: Benchmarking a User-Centered Optimization Agent for Emissions-Aware V2G Energy Arbitrage

A Data-Driven Framework for Sensor-based Anomaly Analysis and Predictive Microclimate Control in Small-scale Potato Storage

Postharvest losses during potato storage remain a serious problem, especially for small-scale storage facilities with limited automation, monitoring, and energy resources. This paper proposes a data-driven cyber-physical framework for sensor-based microclimate analysis and predictive management of small-scale potato st...

Site-Specific Architectural Design of Multi-Story Residential Buildings with Hierarchical Expert Iteration

Purpose: Residential floor-plan design is a combinatorial problem without a closed-form solution. Deep generative models mostly produce images or coarse room boxes of single-story apartments without reference to the plot, and general-purpose large language models lack robust geometric reasoning. We investigate how prel...

ADHD symptoms and efficient learning strategies in college students: the parallel mediating roles of motivation and mind-wandering

A questionnaire survey was conducted among college students from a university in Sichuan Province to examine the relationship between ADHD symptoms and the use of efficient learning strategies, with a focus on the parallel mediating roles of learning motivation and mind-wandering. Of the 919 questionnaires distributed,...

Supervisory Reinforcement Learning for Remote PID Gain Tuning in a Networked Embedded Control System

Modular dual-critic reinforcement learning control for AUV path tracking and collision avoidance

Autonomous underwater vehicles (AUVs) operating in complex and uncertain ocean environments require reliable path-tracking and obstacle-avoidance capabilities to maintain safety in the presence of currents and obstacles. Traditional end-to-end deep reinforcement learning (DRL) methods often struggle with coupled naviga...

EcoTrust-Net: A Causal, Explainable, and Energy-Aware Graph Transformer and Multi-Agent Reinforcement Learning Framework for Intelligent Network Traffic Prediction and Optimization

The increasing complexity of sixth-generation (6G) communication systems, Software-Defined Networking (SDN), edge intelligence, cloud-native infrastructures, and Internet of Things (IoT) environments has intensified the need for intelligent network traffic prediction and optimization capable of supporting low latency,...

Explainable Stability Certification of Reinforcement Learning Control Policies Using Sparse Dynamics Identification

Reinforcement learning (RL) is a promising alternative to classical guidance and control methods; however, the black-box nature of deep neural network policies and the lack of interpretable stability evidence remain barriers to real-world aerospace adoption. This paper presents an a posteriori methodology using Sparse...

Governance-aware autonomous retail coordination in artificial intelligence cities using multi-agent reinforcement learning, blockchain accountability, and federated learning

When autonomous systems take operational authority over urban commerce, accountability and human oversight matter as much as efficiency. We present AAIRM, a governance-aware autonomous retail coordination framework addressing three requirements for trustworthy procurement: tamper-evident decision provenance, data sover...

Vehicle trajectory planning under observational perturbations with robust adversarial reinforcement learning

DEVELOPMENT OF CLASSROOM WELL-BEING STRATEGIES FOR MANAGING BEHAVIOURAL ISSUES AMONG PRIMARY SCHOOL PUPILS USING THE FUZZY DELPHI METHOD

Behavioural issues among primary school pupils such as hyperactivity, excessive classroom noise, low motivation, lack of focus, and disruptive behaviour have become increasingly challenging for teachers in creating an effective learning environment. These behavioural challenges affect pupils’ engagement, classroom well...

Resilient train formation control for ASVs under intermittent communication environment via actor–critic reinforcement learning

To address the train formation task of autonomous surface vehicles (ASVs) operating under intermittent communication environment, this paper investigates the problem of resilient formation robust control for ASVs. First, a resilient distributed leader predictor (RDLP) is designed. By introducing a time-varying adjustme...