reinforcement learning

Knowledge-guided hierarchical reinforcement learning for robust operational control of chiller plants: Field deployment in a hotel building

Transformer-HAPPO: A convergent multi-agent reinforcement learning framework for large-scale single-track railway timetabling

Train–Inference Chain: Hardware-aware structured multi-environment reinforcement learning for efficient adaptation

Service-effort trade-offs in large-scale bike-sharing rebalancing: A hierarchical reinforcement learning framework with imitation-based warm-starting

Graph-based collaborative multi-agent deep reinforcement learning for reconfigurable job shop scheduling

Adaptive job scheduling and resource allocation for industrialized construction processes using digital twin and reinforcement learning

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.

Actuator-aware reinforcement learning control of active suspension systems with implicit road-adaptive reward shaping

Reinforcement learning reshapes cooperation in interaction dependent asymmetric games

Path planning for underwater gliders combining acoustic field propagation characteristics and deep reinforcement learning

Robust reachability within deep reinforcement learning framework

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