reinforcement learning

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...

Role of machine learning in drug discovery and therapeutic protein design

Artificial Intelligence for Smart Grid and Renewable Energy Management: A Review of Techniques, Applications, and Future Directions

Intermittent renewable generation, volatile loads, and concerns over grid stability have become defining features of modern power systems. Addressing these challenges with Artificial Intelligence (AI) and Machine Learning (ML) is attracting growing attention, including through better forecasting, automated demand respo...

Autonomous AI for Electronic Design Automation (AI-EDA): Reinforcement Learning Floorplanning, Graph Neural Routing, and Sub-2nm PPA Optimization

Modern Very Large Scale Integration (VLSI) semiconductor design is confronting unprecedented computational complexity as leading-edge manufacturing descends into sub-2nm Gate-All-Around (GAA) nanosheet nodes and 3D heterogeneous chiplet packaging. System-on-Chip (SoC) architectures integrating over 100 billion transist...

Deterministic 4D Spacetime Geodesic Planning on Hypercomplex Toroidal Manifolds

Dynamic obstacle avoidance and 4D spacetime pathfinding present severe computational bottlenecks in autonomous robotics. Classical time-expanded graph architectures (e.g., Space-Time A*) suffer from catastrophic state explosion O(|V| x T_horizon), exhausting memory over extended planning horizons. Local continuous opti...