reinforcement learning

HiRAD: A Flexible Large-Scale AGV Routing System

HiRAD is proposed, a hierarchical RL framework for continuous-space AGV routing with real-time guarantees that reduces makespan by 45 percent to 63 percent and shortens end-to-end runtime.

Reinforcement learning-based intelligent control algorithm for energy-efficient and emission-aware optimization in industrial power systems

This paper proposes a novel Reinforcement Learning–Based Intelligent Control (RL-IC) algorithm for energy-efficient and emission-aware optimization in industrial power systems. Unlike conventional rule-based or model-predictive controllers that rely on fixed dynamics, RL-IC employs a hierarchical dual-objective learnin...

Teachers’ Experiences in Building Motivating and Anxiety-Reducing Language Learning

Adaptive E2E Monitoring Path Selection with Deep Reinforcement Learning in Multi-domain Optical Networks

Supplemental Materials for “Planning for Leakage-Aware Collaborative Carbon Mitigation: Optimizer-Informed Multi-Agent Reinforcement Learning in Shandong’s Dual Metropolitan Areas”

Supplementary materials data set for the paper tilted “Planning for Leakage-Aware Collaborative Carbon Mitigation: Optimizer-Informed Multi-Agent Reinforcement Learning in Shandong’s Dual Metropolitan Areas”

Supplementary document for Reinforcement learning optimization of a ⁶Li magneto-optical trap: DDPG versus SAC - 8037132.pdf

8 pages and 7 sections

Navigating the smart tourism era: a systematic review of how intelligent systems are shaping the future of travel

Tourism recommendation systems use artificial intelligence (AI), machine learning (ML), and data analytics to support travel decisions. This systematic literature review examines decision types, disciplinary distribution, themes, and research gaps in AI-based tourism recommendation research. Following the Preferred Rep...

Explainable reinforcement learning for smart production scheduling in automotive manufacturing

Supplementary document for Reinforcement learning optimization of a ⁶Li magneto-optical trap: DDPG versus SAC - 8037132.pdf

8 pages and 7 sections

Balancing solution quality and runtime in modular mobile hospital location: a hybrid approach of deep reinforcement learning and integer programming

A graph-based reinforcement-learning framework for logical qubit allocation in fault-tolerant quantum architectures

A graph-based reinforcement-learning framework is proposed in which a graph neural network is first pretrained to predict the ancilla-qubit cost of circuit-allocation pairs from allocation-aware circuit graphs, and the resulting encoder is transferred to a reinforcement learning agent that assigns tiles one at a time.

Toward trustworthy adaptive software reliability engineering: An explainable deep reinforcement learning framework

Ensuring the dependable operation of modern software systems under dynamic and non-stationary operating conditions remains a major challenge in software reliability engineering. Although recent deep reinforcement learning (DRL)-based approaches have demonstrated promising capabilities for closed-loop adaptation of soft...