reinforcement learning

Artificial Intelligence in Environmental Monitoring, Pollution Control, and Low-Carbon Management

Air pollution, water contamination, soil degradation, solid waste accumulation, and carbon emissions are increasingly interconnected, posing common challenges to environmental engineering, including diverse monitoring targets, heterogeneous data sources, competing control objectives, and delayed management responses. T...

Data and Code Supporting “Physics-Guided Surrogate Learning Enables Zero-Shot Control of Turbulent Wings”

This dataset supports the study “Physics-guided surrogate learning enables zero-shot control of turbulent wings.” The research investigates whether control policies trained in computationally tractable turbulent channel flows can be transferred directly, without additional training, to the turbulent boundary layer over...

AI-Powered Logic Gate Optimization for Efficient Electronic Design

Abstract The continuous development of modern electronic devices has resulted in increasingly complex Integrated Circuits (ICs). As the number of logic gates and circuit components increases, designing efficient electronic systems becomes more challenging. Engineers need to consider several important factors, including...

RL-Based Adaptive Cyber Defence System

In the current connected digital world, cyber defence can no longer function in the traditional and signature-based protection, but it should adopt machine-speed adaptivity and intelligence. The ruthless development of threats, which involves zero-day exploits and polymorphic malware, among others, makes the convention...

Adaptive Traffic Signal Control at a Single Intersection Using Deep Reinforcement Learning: A SUMO-Based Comparative Study

Urban road networks lose a large amount of time and fuel to congestion at signalised intersections, and conventional fixed-time controllers cannot react to the changing and uneven traffic demand seen in practice. Reinforcement learning (RL), and in particular deep reinforcement learning (DRL), has been proposed as a wa...

Adaptive Traffic Signal Control at a Single Intersection Using Deep Reinforcement Learning: A SUMO-Based Comparative Study

Urban road networks lose a large amount of time and fuel to congestion at signalised intersections, and conventional fixed-time controllers cannot react to the changing and uneven traffic demand seen in practice. Reinforcement learning (RL), and in particular deep reinforcement learning (DRL), has been proposed as a wa...

An artificial intelligence-based two-stage framework for dynamic pricing and demand-side management in smart grids

Rapid urbanization, the rapid growth of electricity demand, and the widespread adoption of electric vehicles have increased the complexity of managing modern power systems. Conventional fixed pricing mechanisms are increasingly insufficient to ensure efficient energy utilization, peak load mitigation, and system resili...

Auditing Single-Agent Reinforcement Learning for EV Charging Assignment: A Protocol-Amended Comparison of Trained, Untrained, and Heuristic Policies

Deep reinforcement learning (DRL) is widely assumed to outperform simpler rule-based and tabular baselines for sequential decision problems. We test this assumption for electric vehicle (EV) charging assignment using Simulation of Urban MObility (SUMO) simulations of real Rabat and Tangier road networks, with a protoco...

AI-Driven Data Governance Framework for Enterprises in Cloud Environments: Design, Implementation, and Enterprise Evaluation

Challenges cloud-based enterprise data governance is encountering include data growth, regulatory changes, and the inflexibility of traditional rule-based systems. This systematic literature review, based on PRISMA guidelines, analyses 67 peer-reviewed papers (2019–2026) under three research questions related to AI-dri...

Arcstone Executive Epistemic & Execution Series: Human-First Trust, Machine-First Execution, Hybrid Alignment, Machine-Native Authority, Admissibility Science, Isomorphic Architecture, and Literature Synthesis (EXEC01–EXEC04, CORE01–CORE02, META01, LIT003)

===============================================================================ARCSTONE EXECUTIVE EPISTEMIC & EXECUTION SERIES (EXEC01–EXEC04, CORE01–CORE02, META01, LIT003)Primary Suite DOI Anchor: 10.5281/zenodo.22665852Master System Hash Anchor: A-77-DELTA-SHIELD-LOCKEDCanonical Handle: @admissibilityscience========...

AI-Driven Microarchitectural Optimization and Automated Datapath Synthesis in Modern Computer Architecture

Artificial Intelligence (AI) can support modern computer architecture by automating the exploration and optimization of microarchitectures and datapaths. Techniques such as machine learning and reinforcement learning can help predict performance, improve power efficiency, and optimize hardware resources. Its integratio...

Asynchronous multi-agent reinforcement learning for network-wide traffic signal control via stackelberg game prioritization