Multi-agent switching energy management strategy for fuel cell hybrid vehicles using an enhanced deep reinforcement learning algorithm

Reinforcement Loads Prediction of Geosynthetic-Reinforced Soil Structures Using Explainable and Nonexplainable Machine Learning Approaches

This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soil structures. These models were developed to enhance both predictive accuracy and model interpretability by incorporating state-of-the-art optimization algorithms and explainable machine learning frameworks. A comprehensive evaluation was conducted using 10-fold cross-validation, and the proposed models were benchmarked against previously developed AI models from literature, as well as traditional and semiempirical approaches such as Rankine, Coulomb, and K -stiffness. Among the proposed models, ASSA-MBM consistently achieved the best performance, recording the lowest testing root mean squared error (0.617), the highest correlation coefficient ( R = 0.918 ), and the highest reference index ( RI = 0.951 ). Additionally, the ORF model offers transparency by generating mathematical regression equations, which are crucial in geotechnical engineering.

Uncertainty-aware reinforcement learning for joint optimization of data center operations and electricity market bidding

Dynamic obstacle avoidance for autonomous vehicles in complex traffic environments: A guidance-enhanced deep reinforcement learning approach

Deep reinforcement learning for adaptive rack repositioning and pod sequencing in robotic mobile fulfillment systems via multi-fidelity simulation optimization

A multi-agent deep reinforcement learning framework for coordinated urban traffic signal and parking lot exit control

Tri-driven graph reinforcement learning for dynamic root cause tracing in UF-RO water treatment processes