reinforcement learning

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

Vulnerability-aware reinforcement learning scheduling of AI data centers under typhoon-induced thermal coupling

The rapid growth of AI data-center loads, amplified by typhoon-induced thermal coupling between workloads and cooling systems, poses new challenges to transmission grid vulnerability assessment and scheduling. This paper develops a vulnerability-aware reinforcement-learning (RL) scheduling framework that integrates a m...

PANES: Policy-guided Asymmetric Nash Equilibrium Search

Trick-taking card games with mandatory bidding confront reinforcement learning agents with a distinctive two-phase problem: each player must commit to a numeric bid before any cards are played, and whether that bid turns out to be correct hinges on adversarial interactions unfolding over many subsequent tricks. Judgeme...

A deep learning and blockchain enabled adaptive routing framework for secure Vehicular Ad Hoc Networks

Vehicular Ad hoc Networks (VANETs) are one of the most important enablers of Intelligent Transportation Systems (ITSs), however, their dynamic topologies and susceptibility to malicious activities provide a serious challenge to the routing efficiency and security. To address these challenges, this paper introduces a ne...

Reinforcement Learning in Serious Games: an Exploratory Analysis

Step-Level Gradient Masking for GRPO: Selective Optimization of Reasoning Trajectories

Group Relative Policy Optimization (GRPO) has recently emerged as an effective algorithm for Reinforcement Learning from Verifiable Rewards (RLVR), allowing for improvements in logical reasoning, more specifically in mathematical reasoning in large language models (LLMs) without supervised reasoning traces. It does, ho...

Reinforcement Learning: Emerging Trends and Future Research Directions

Active suspension preview control via deep reinforcement learning with hybrid synchronizing policy and hierarchical road perception

The rapid advancement of vehicle electronic control systems has provided favorable support for enhancing the dynamic response and control performance of suspension systems. Conventional control strategies based on suspension state feedback are limited in their active adjustment capability due to the lack of utilization...