reinforcement learning

Deep Learning for Proxy Zero-Day Intrusion Detection: A Systematic Review, Taxonomy, and Evaluation Framework

Safety-Certified Hierarchical Control of DC Microgrids via Continuous Deep Reinforcement Learning and Covariance-Weighted Sliding Mode Synthesis

This paper presents a hierarchical safe control approach for DC microgrids, combining learning-based energy management with rigorous constraint enforcement. The main novelty is a covariance-aware coupling mechanism that simultaneously informs CBF constraint tightening, sliding-mode gain scheduling, and DRL state augmen...

Distinct Patterns of Adaptive Decision-Making in Mouse Models of Neurodevelopmental Disorders

Neurodevelopmental disorders (NDDs) arise from diverse genetic alterations that can affect adaptive decision-making, but quantifying specific components of cognitive processes remains challenging. We developed a sequence of deterministic and probabilistic reversal learning tasks using an automated operant platform and...

ALEX-GWO: Reinforcement-learning-driven Adaptive Grey Wolf Optimization with Landscape-aware Local-optima Escape for Large-scale Global Optimization

Large-scale global optimization exposes a structural weakness of the grey wolf optimizer: its convergence coefficient follows a schedule fixed in advance, the three leading wolves collapse into a single attraction basin long before the budget is spent, and that loss of diversity is neither detected nor repaired.Publish...

Intelligent Recognition of Joint Positions in Surrounding Rock of Tunnels Based on Interpretable Machine Learning Models

Machine Learning-Driven Optimization of Multiphase Chemical Processes for Energy-Efficient and Sustainable Manufacturing

There are many industrial chemical manufacturing processes that involve multi-phase chemistry, such as gas–liquid, liquid–liquid, gas–solid, solid–liquid and three-phase systems, and these are among the most energy-intensive and complex unit processes to optimise, due to the complex, nonlinear and often unmeasurable in...

Fault Recovery Strategy for Urban Distribution Networks with Distributed Photovoltaic Generation Based on Dual-Dimensional Graph Reinforcement Learning

With the rapid development of renewable energy, the large-scale integration of distributed photovoltaic (PV) generation into urban distribution networks has resulted in increasingly complex topologies and frequent PV power fluctuations, posing challenges for fault-state assessment and time-constrained restoration decis...

Coordinated ramp metering under asymmetric traffic demand: a continuous deep reinforcement learning approach

Asymmetric ramp demand is common in multi-ramp freeway corridors and can complicate coordinated ramp metering. Existing approaches may not explicitly represent differences in short-term queue growth across ramps, while discrete metering actions can limit control resolution. This study proposes a soft actor–critic-based...

An adaptive congestion-aware graph neural routing framework with hybrid microcoded event-driven control for network-on-chip systems

Network-on-Chip (NoC) architectures have become a fundamental communication backbone for many-core processors, artificial intelligence accelerators, edge-computing platforms, and high-performance embedded systems. However, achieving efficient packet routing in large-scale NoCs remains challenging due to dynamic traffic...

A Review of Virtual Power Plant Technology and Practice Development in China: Progress, Challenges, and Outlook

As an important vehicle for resource integration in the construction of new power systems, the virtual power plant (VPP) has moved from the demonstration and pilot stage into a critical transition period of standardized construction and market-oriented operation. This paper systematically reviews the technological deve...

Measurement-in-the-Loop Reinforcement Learning for Regional UAV Communication Enhancement

This paper investigates a measurement-driven rotary-wing unmanned aerial vehicle (UAV)-enabled wireless communication system serving multiple ground users. Unlike conventional studies relying on simplified theoretical channel models, we derive the air-to-ground (A2G) path loss and shadow-fading models from software-def...

The need for uncertainty: why probabilistic LSTMs are key to improving flood predictions and enabling learned warning rules

Deterministic model predictions can struggle to adequately capture extreme events such as floods and droughts, which are of particular importance in hydrology. This limitation arises because deterministic models collapse the conditional discharge distribution to a single point estimate. Probabilistic modeling provides...