reinforcement learning

Role-Aware Representation and Uncertainty-Driven Policy Regularization for UAV Swarm Air Combat

Database and code for punching shear of FRP-reinforced flat slabs: a leakage-aware benchmark

This dataset accompanies the manuscript "Do machine learning models outperform design codes for the punching shear strength of FRP-reinforced flat slabs? A leakage-aware benchmark with explainable analysis." It contains a database of 87 punching shear tests on interior slab–column connections reinforced with glass or c...

Control-Guided Reinforcement Learning for Cooperative Energy Management

Dataset associated with the open access publication "Control-Guided Reinforcement Learning for Cooperative Energy Management" by Isabela Fons Moreno-Palancas, Rubén Ruiz Femenia, Raquel Salcedo Díaz, José A. Caballero, Chanona A del R. (Systems and Control Transactions. 2026, 6, 1558-1564). The dataset includes results...

Identifiability of Ensemble Disagreement under One-Step Distillation

Model-based reinforcement learning gates imagined transitions by how much an ensemble disagrees, and diffusion world models are distilled into one-step students to make that imagination affordable. Whether distillation preserves the score the gate reads is rarely checked; we show that it need not. Matching the teacher'...

Training-Data Axes in Imitation Learning Cascade into Hybrid Reinforcement-Learning Fine-Tuning: A Leave-One-Out Bridge Study Under Matched and Cascade Evaluation

Hybrid imitation-learning-to-reinforcement-learning (IL→RL) driving stacks are typically evaluated against a single fixed IL prior, leaving open whether IL training-data quality determines downstream RL outcomes and whether hybrid actuator decoupling (IL steers, RL controls only speed) isolates the speed controller fro...

EG-CT-MADDPG: Source Code, Trained Models, and Validation Data for Dynamic Truck–Drone Delivery Scheduling

Source code, trained model checkpoints, input instances, and reference results for a dynamic truck–drone delivery scheduling method based on multi-agent deep reinforcement learning. The package accompanies the manuscript and supports reproduction of the reported experiments.

Hierarchical collaborative multi-agent reinforcement learning for hot-rolling production planning with order-splitting flexibility

In mass personalized hot rolling, intricate constraints cause load imbalances and low order fulfilment. While order splitting alleviates these bottlenecks, it increases changeover frequency and planning complexity. We propose a bi-level model: the upper level optimizes production cost and time, while the lower minimize...

Error Attribution as a Resource-Allocation Principle for Quantum Error Correction: Sensitivity-Guided Noise Reduction and Its Analytic Limits

Logical error rate is the standard benchmark for quantum error correction (QEC), but it is an aggregate quantity: it says nothing about which circuit components actually drive logical failure. Recent work introduced an error attribution scheme that computes per-component sensitivities $\partial P_L/\partial p_i$ of the...

Systems oriented review on artificial intelligence as a sustainability enabler in renewable energy modeling and energy transition pathways

Artificial intelligence (AI) has emerged as a critical enabler of renewable energy system development, offering new capabilities for forecasting, optimization, predictive maintenance, and real-time control. However, existing review studies remain fragmented across individual renewable-energy technologies or algorithmic...

RAC Beam Shear-Capacity Dataset for Study-Independent and Uncertainty-Aware Machine Learning

This dataset contains 229 experimental observations of recycled aggregate concrete (RAC) beams used for machine-learning-based prediction of shear capacity. The dataset was independently digitized, standardized, quality-screened, and regrouped at the experimental-study level from the published RAC beam database reporte...

Reinforcement Learning-Based and Hash-Based Lossless Compression of Genomic Sequences

A novel graph reinforcement learning framework for multilevel flexible scheduling in fuzzy production environments