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

LLM powered adaptive traffic signal control via knowledge-enhanced prompt and reinforcement learning fine-tuning

Reinforcement learning-enhanced barrier function super-twisting sliding mode control for bidirectional electric vehicle charging with superconducting magnetic energy storage

LOGO: A framework for language-based goal decomposition and skill recomposition in reinforcement learning

Prescribed performance optimal control for autonomous aerial–ground heterogeneous systems via safe reinforcement learning

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

Seismic Control of a Smart Base-Isolated Building with Nonlinear Behavior Using Deep Reinforcement Learning

DRL is highlighted as a promising data-driven strategy for robust and adaptive control of nonlinear structural systems under partial observability by addressing a critical limitation of passive systems and accelerates the decay of residual vibrations.