reinforcement learning

Code for "Multi-criteria prioritized reinforcement learning for autonomous underwater vehicle navigation in 3D multi-node environments"

Artificial Intelligence–Driven Intrusion Detection Systems: A Systematic Review of Machine Learning, Deep Learning, and Adaptive Ensemble Approaches

This is a critical systematic review of 40 peer-reviewed articles published between 2021 and 2025 on artificial intelligence-enhanced Intrusion Detection Systems (IDS).To maintain rigour and transparency in the review, the PRISMA 2020 and Kitchenham guidelines are adhered to.The appropriate literature was also searched...

sruthijanga/EANAS-GreenAI: EANAS-GreenAI

EANAS-GreenAI v1.0.0 EANAS-GreenAI is a research implementation of Energy-Aware Neural Architecture Search for Edge-AI Deployments. It combines CNN architecture search, reinforcement learning, and evolutionary optimization to balance model accuracy, inference latency, and energy consumption. Key Features: Five-block CN...

Editorial: The role of cellular subtypes in striatal activity

The basal ganglia are central to the selection and execution of actions, integrating diverse information to shape motor, cognitive, and motivational behaviors. Within this network, the striatum occupies a key position where convergent cortical, thalamic, and neuromodulatory inputs are transformed into patterns of activ...

A hierarchical reinforcement learning framework for high-efficiency control of turbine-assisted fuel cell air supply systems

Event-Based Reinforcement Learning for Cyber-Physical Energy Systems – Smart Buildings, Smart Grid, and Smart Cities

DiffLyap-MEC: Diffusion-Guided Lyapunov Optimization for Joint Offloading, Semantic Compression, and Resource Allocation in ISAC-Enabled 6G Edge Computing

Integrated sensing and communication (ISAC), semantic communication, and mobile edge computing (MEC) jointly create a high-dimensional, non-convex, and time-coupled resource allocation problem in sixth-generation (6G) networks that existing heuristics and deep reinforcement learning (DRL) methods solve either slowly or...

ENGINEERING DECISION SYSTEMS FOR METAL FABRICATION: VYACHESLAV SHARGAEV'S APPLIED AI APPROACH

Closed-loop artificial intelligence is increasingly claimed for metal-fabrication systems that combine reinforcement-learning-based process control with vision-based quality inspection. This review organizes the reinforcement-learning, computer-vision, and production-economics literature bearing on metal cutting around...

Topology-aware graph reinforcement learning for generalizable cascading failure mitigation in hybrid AC/DC power systems

Cascading failures in hybrid AC/DC power systems can propagate rapidly through DC-side disturbances, AC branch overloads, topology changes, and islanding, making emergency mitigation a constrained sequential decision-making problem under evolving network structures. This paper proposes a topology-aware graph reinforcem...

Hybrid AC/DC Distribution Network Planning Under Renewable Ramp Stress Considering Network-Constrained Flexibility via RL with Feasible Projection

High renewable energy penetration intensifies net-load ramping and increases the demand for operational flexibility in distribution networks. However, existing hybrid AC/DC distribution network planning studies rarely consider both renewable ramp stress and the network deliverability of available flexibility. Furthermo...

Electronic System-Level Optimization of Network Slicing and Resource Allocation for 6G URLLC Applications

In this paper, we propose a complete mathematical framework for optimizing network slicing and resource allocation at the electronic system-level (ESL) of 6G ultra-reliable low-latency communication (URLLC). We present a multi-dimensional, non-convex mixed-integer stochastic optimization problem that jointly optimizes...

Hybrid Path Planning and Reinforcement Learning-Based Magnetic Guidewire Navigation in Cardiac Intervention

Magnetically driven microrobots have shown significant potential in biomedical applications, including endovascular intervention, targeted therapy, and drug delivery, particularly in the treatment of cardiovascular diseases. Conventional vascular interventional procedures typically rely on physicians to manually manipu...