reinforcement learning

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

CoEvoKG: Co-Evolving Knowledge Graphs with Self-Evolving Search Agents

CoEvoKG is introduced, a framework that turns a knowledge graph into both a source of verifiable training tasks and a persistent evidence memory for agent evolution, closing the loop between model self evolution and knowledge accumulation.

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

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms.

ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in NSCLC and BRCA.

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.

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

The first comprehensive benchmarking framework specifically designed to accommodate inter-dataset heterogeneity is presented, finding that well-designed small datasets can match or even surpass the performance of larger benchmarks, suggesting that different metrics are applicable to different datasets/testing scenarios.

Navigating committor landscape of biomolecules with a general pairwise interaction model

A novel committor learning framework grounded in the AlphaFold 3 paradigm is proposed that elucidates how ligand substituents regulate the ratio between distinct binding pathways, offering new perspectives for structure-based drug design.

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.

The Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein-ligand distances and reconstructs binding poses; and RTMScore, an accurate scoring model that learns residue-atom distance distributions for affinity prediction.

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