reinforcement learning

A Hybrid Genetic Reinforcement Learning Technique for Intelligent Cluster Head Selection in WSNs

In Wireless Sensor Networks (WSNs), selection of Cluster Head (CH) in view of the optimum performance is a difficult task in view of constraints of energy resources, dynamic energy state of the network and dynamic data communication requirements, etc. Here, optimization of these can make better solutions. A Hybrid RL-G...

Reinforcement Learning in Machine Learning

Reinforcement Learning (RL) is a machine-learning approach for problems in which decisions are made repeatedly and their quality becomes clear through feedback. Instead of depending on a fixed collection of labelled answers, an RL agent interacts with an environment, observes its changing state, receives rewards or pen...

MF/UF/NF/RO Desalination Design Framework — Egypt Case Study (with AI-based design optimization)

Physics-based design framework for brackish-groundwater and seawater RO desalination (Egyptian feed-water presets), with ML surrogates, Bayesian optimization, NSGA-II multi-objective optimization, a deep-learning large-scale surrogate, a reinforcement-learning multi-site allocator, and external-validation and statistic...

Quantum Game-Theoretic Optimization with Ramanujan-Inspired Regularization for Supply-Chain Resilience: A Component-Level Study and Reference Architecture

I am a Quantum AI researcher and computer science educator with 25 years of industry and teaching experience. My expertise includes Quantum Computing, Quantum Machine Learning, Quantum Reinforcement Learning, QNNs, VQAs, Qiskit, and Quantum Error Correction. I hold a Ph.D. in Quantum AI and actively pursue research int...

Physics-Guided Graph Safe Reinforcement Learning for High-Fidelity and Scalable Alternating Current Optimal Power Flow

The alternating current (AC) optimal power flow (AC-OPF) problem is crucial for efficient power system operation, yet it remains challenging due to its nonlinear, nonconvex structure and numerous operational constraints. This paper introduces a Physics-guided Graph Safe Reinforcement Learning (PGSRL) framework that pro...

A GENETIC ALGORITHM-BASED ADAPTIVE COVERAGE METHOD USING HEXAGONAL AREAS FOR WSN WITH DEEP REINFORCEMENT LEARNING

Simulation Data and MATLAB Code for "Hierarchical Formation Tracking of USV Swarms with Dynamic Event-Triggered Coordination and Reinforcement Learning"

This dataset contains the MATLAB simulation code and numerical results supporting the manuscript entitled “Hierarchical Formation Tracking of USV Swarms with Dynamic Event-Triggered Coordination and Reinforcement Learning.” The files reproduce the numerical results reported in Figs. 1–7 and Tables 2–3 of the manuscript...

Deep reinforcement learning-driven cross-layer optimization of IRS-assisted NOMA with blockchain for secure 6G oriented networks spectrum management

The rapid development of connected devices and data intensive applications is demanding the need for smart spectrum management mechanisms in emerging 6G Networks. Traditional centralized spectrum allocation schemes often find it challenging to cope with excessive wireless environments and larger-scale distributed netwo...

Development of a Reinforcement Learning-Based Decision Support System for Optimizing Tactical Strategies in Professional Team Sports

Introduction: Tactical decision-making in professional team sports represents one of the most complex challenges for coaches and analysts, influenced by numerous dynamic factors. Traditional tactical analysis methods rely primarily on human observation and descriptive statistics, lacking sufficient capability for real-...

V2I-Enhanced State Representation for Adaptive Traffic Signal Control Using Deep Q-Networks

Traffic congestion remains a major challenge for intelligent transportation systems. Vehicle-to-Infrastructure (V2I) communication provides a promising foundation for adaptive traffic signal control by enabling enhanced real-time traffic detection. This paper proposes an adaptive traffic signal control framework that e...

Microlearning in luxury hospitality: Perceptions and practical impacts in five-star hotels in Qatar

This study explores the adoption and managerial value of microlearning in five-star hotels in Qatar, focusing on its role in operational training. Grounded in adult learning theory, the Technology Acceptance Model, and organizational learning, a qualitative phenomenological approach was used. Semi-structured interviews...

The Description–Experience Gap in Loss Aversion

Humans make different decisions under risk when they acquire information about their options through experience than when they learn it from a summary description. Research on this description–experience gap has largely focused on differences in probability weighting—for instance, how low-probability events are reflect...