reinforcement learning

Ray-Traced D2D Link Scheduling: Dataset, Code and Results for "Graph Neural Networks for D2D Link Scheduling: Centralized and Distributed Schedulers Evaluated with Sionna Ray-Traced Channels"

Supporting dataset, source code, trained models and result files for the manuscript "Graph Neural Networks for D2D Link Scheduling: Centralized and Distributed Schedulers Evaluated with Sionna Ray-Traced Channels" (Sensors, MDPI; revised version). The archive contains 24,000 device-to-device network snapshots generated...

A Context-Aware Intelligent Agent for Adaptive Browser Resource Orchestration

Modern web browsers consume a significant amount of system memory when multiple tabs remain open, causing reduced performance, slower response times, and occasional browser crashes. Managing tabs manually is inefficient and interrupts the user’s workflow. This paper presents ALIA (Adaptive Load Intelligent Agent), a Ma...

Da Nang International Airport TMYx 2011–2025 Weather Dataset

This dataset contains the Da Nang International Airport (WMO 488550) TMYx 2011–2025 weather data used as climatic input for the study “Autonomous Kinetic Facades: A Bio-inspired Framework using Shape-Memory Alloy (SMA) Actuators and Reinforcement Learning for Dynamic Building Thermoregulation.” The dataset was obtained...

Deterministic Spatial Planning on Hypercomplex Manifolds: 1-Pass Maze Navigation with Zero Backpropagation and 53 KB Memory Footprint

Standard Reinforcement Learning (RL) and Frontier Foundation Models face severe structural bottlenecks in spatial planning. Deep RL algorithms (e.g., PPO, DreamerV3) suffer from extreme sample complexity (10^6–10^8 interactions) due to gradient dilution across delayed reward horizons, while Large Language Models (LLMs)...

Autonomous Scientific Discovery via Exploration Schema Libraries: Replicating Scientists' Problem-Space Metacognition

Current Artificial Intelligence for Science (AI4S) frameworks excel at pattern recognition within massive datasets but fundamentally lack causal reasoning and the capacity for autonomous theoretical innovation. To address this limitation, we introduce a novel paradigm that shifts the focus from learning scientific data...

Learning to Order: GNN-Based Deep Reinforcement Learning Policies for Autoregressive Graph Models

THE ROLE OF ETHICS IN CRIMINAL JUSTICE EDUCATION: PREPARING STUDENTS FOR THE MORAL CHALLENGES OF LAW ENFORCEMENT

Ethics is essential to developing competent, accountable, and professional criminal justice practitioners. This qualitative phenomenological study explored how ethics is understood, integrated, and experienced in the Bachelor of Science in Criminology program of Visayas State University–Tolosa. Data were gathered throu...

Age of digital twin-driven deep reinforcement learning for smart task offloading in vehicular fog computing

A Context-Aware Intelligent Agent for Adaptive Browser Resource Orchestration

Modern web browsers consume a significant amount of system memory when multiple tabs remain open, causing reduced performance, slower response times, and occasional browser crashes. Managing tabs manually is inefficient and interrupts the user’s workflow. This paper presents ALIA (Adaptive Load Intelligent Agent), a Ma...

Project TALOS: Tactical Agentic Literature Orchestration System

Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 18 academic APIs.

A Digital Twin Framework for Autonomous Kinetic Facades: Bio-inspired SMA Actuation with Reinforcement Learning Control for Tropical Climates

Building envelope automation is limited by heavy motor-driven shading and reactive rule-based controllers. This paper presents an autonomous Digital Twin framework for kinetic facades, integrating bio-inspired Shape-Memory Alloy [SMA] actuation with a Reinforcement Learning [RL] control policy. Conceived as a Robotic S...

LEARNING-BASED OPTIMIZATION OF DIGITAL HARDWARE: AI METHODS FOR SYNTHESIS, PHYSICAL DESIGN, AND VERIFICATION

Abstract The increasing complexity of digital systems has made hardware optimization a learning problem as much as an algorithmic one. Conventional electronic design automation (EDA) flows depend on manually engineered heuristics that may not generalize across architectures, workloads, and technology nodes. This journa...