reinforcement learning

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...

A Maximum Entropy Multi-agent Reinforcement Learning Algorithm Based on Gated-Self-attention

AI-Based Dynamic Pricing in Digital Commerce: Implications for Consumers and Retailers

Artificial intelligence (AI) is reshaping pricing in digital commerce. Retailers can now combine demand, inventory, competitor prices and customer signals to revise prices quickly. This review examines how AI-based dynamic pricing works and what it means for consumers and retailers. It separates market-responsive prici...

Failure-resilient narrative-coherent sports video summarization under multimodal asynchrony: a context-aware swin reinforcement learning framework

Design, management, and optimization of networks using machine learning, deep learning, and reinforcement learning

Reinforcement learning-based optimized search for extraterrestrial intelligence (SETI)