reinforcement learning

Transdiagnostic latent factors dissociating depression and anxiety through reinforcement learning

The comorbidity of depression and anxiety has long been recognized. While mainly characterized as mood dysregulation, depression and anxiety symptoms are also manifested in learning and decision-making deficits. However, the specific cognitive mechanisms that are common to both disorders, as well as those that distingu...

Large Language Model-Enhanced Multi-Agent Reinforcement Learning for Autonomous Enterprise Workflow Orchestration via Graph Semantic Routing

# LeMARLA **Large Language Model-Enhanced Multi-Agent Reinforcement Learning for Autonomous Enterprise Workflow Orchestration via Graph Semantic Routing.** LeMARLA couples an LLM-driven task decomposition module, a heterogeneous graph attention routing module over tasks, employees and skills, and a CTDE multi-agent rei...

Feature-Level Fusion of RGB Camera and 2-D LiDAR for Local Occupancy Estimation and Reactive Navigation: A Controlled Simulation Study

This preprint presents a controlled, systems-level simulation study of feature-level fusion of RGB camera and 2-D LiDAR for local occupancy estimation and reactive navigation of an indoor mobile robot. The system integrates ROS 2, PyTorch and Gazebo in a modular perception-to-navigation pipeline. Three perception confi...

Hierarchical reinforcement learning for load restoration of distribution networks under typhoon disasters

Feature-Level Fusion of RGB Camera and 2-D LiDAR for Local Occupancy Estimation and Reactive Navigation: A Controlled Simulation Study

This preprint presents a controlled, systems-level simulation study of feature-level fusion of RGB camera and 2-D LiDAR for local occupancy estimation and reactive navigation of an indoor mobile robot. The system integrates ROS 2, PyTorch and Gazebo in a modular perception-to-navigation pipeline. Three perception confi...

A hierarchical reinforcement learning and predictive control framework for automated multi-variable anesthesia

BACKGROUND AND OBJECTIVE: Automated multi-variable anesthesia must balance conflicting objectives amid patient variability, safety constraints, and partial observability. The state-of-the-art robust approach uses a Genetic Algorithm (GA) to optimize Generalized Predictive Control (GPC) parameters across a population, b...

Deep reinforcement learning for simulated autonomous heat stress mitigation in dairy cattle

Heat stress significantly constrains dairy cattle productivity and welfare, compromising milk production and fertility. Yet, despite advances in heat stress detection and prediction, conventional mitigation strategies still rely on fixed thresholds, overlooking inter-animal variability and cumulative thermal load. In t...

Joint optimization of STAR-RIS phase coefficients and power allocation by imitation-guided multi-agent deep reinforcement learning in SWIPT-NOMA IoT networks

A multi-level framework for capacity adjustment and flexible decision-making under data constraints in production planning and control

A five-axis taxonomy is developed from 55 studies selected through a systematic Scopus review of CA research published between 2020 and 2025 and the Data-Aware Capacity Adjustment Framework is introduced as a seven-step decision-support artifact that screens CA options.

EDA-env: A Reinforcement Learning Environment for RTL-to-GDS Design with Independent Scoring

Task generator, independent scorer, 103 qualified RTL-to-GDS task packages on ASAP7, Nangate45 and Sky130HD, the scorer regression suite (143 probes), and the recorded Kimi-k3 episodes (patches, trajectories, sealed measurements) reported in the ICCAD 2026 paper "EDA-env: A Reinforcement Learning Environment for RTL-to...

EDA-env: A Reinforcement Learning Environment for RTL-to-GDS Design with Independent Scoring

Task generator, independent scorer, 103 qualified RTL-to-GDS task packages on ASAP7, Nangate45 and Sky130HD, the scorer regression suite (143 probes), and the recorded Kimi-k3 episodes (patches, trajectories, sealed measurements) reported in the ICCAD 2026 paper "EDA-env: A Reinforcement Learning Environment for RTL-to...

MudGym

A reinforcement learning environment for MUD2.