reinforcement learning

Deep Reinforcement Learning with State Uncertainty Estimation for Continuous Control of Dynamic Complex Systems

A Deep Reinforcement Learning framework for Continuous Control with State Uncertainty Estimation based on the Multi-Agent Policy Collaborative Optimization (MAPCO) mechanism is proposed, enabling robust state representation and stable policy optimization through distributed information exchange.

MADRL‑SEA: Multi‑agent Deep Reinforcement Learning for Cruise‑Cabin Floorplan Generation

RoboSuite Grasping

This repository contains the source code accompanying the paper “Noise Scheduling and Out-of-Distribution Generalisation in Deep Reinforcement Learning for 6-DOF Robotic Grasping.” The work evaluates three off-policy deep reinforcement learning configurations for a 6-DOF robotic grasping task using a Universal Robots U...

Prequential posteriors

Abstract Data assimilation is a fundamental task in updating forecasting models upon observing new data, with applications ranging from weather prediction to online reinforcement learning. Deep generative forecasting models (DGFMs) have shown excellent performance in these areas, but assimilating data into such models...

Verified Deep Learning with Lean 4

A formal verification of the math of modern deep learning, written as a Lean 4 blueprint project and shipped as the code it proves. Mathlib's fderiv is the foundation; every layer's backward is derived as a vector-Jacobian product rather than asserted -- dense, ReLU, softmax cross-entropy, 2D convolution, max-pool, Bat...

A Learnable Sparse Attention Graph Architecture for Heterogeneous Multi-UAV Air-to-Ground Mission Planning

This study provides an innovative and effective method for mission planning of heterogeneous multi-UAV systems in partially observable adversarial environments and outperforms the best-performing baseline algorithm by more than 20% across all operational performance metrics.

Reproduction code and screened corpus for a survey of evaluation design in reinforcement learning-based route guidance

Reproduction material for the survey "Study-Area Boundaries in Reinforcement Learning-Based Route Guidance: A Survey of Evaluation Design and ReportingRequirements" by Donghoun Lee. The archive contains the grid, benchmark and SUMO environments, the planner, the training code, the screened corpus withthe recorded field...

Reinforcement Learning for Game Playing

Progress in educational theory and translational implementation of simulation-based education for healthcare professionals: an integrative analysis from theory to effectiveness

This review offers a practical selection matrix and implementation governance model that assists educators in designing scalable and equitable SBE, supports administrators in resource planning and quality assurance, and guides accreditors in aligning evidence with entrustment and standards.

Supplemental Materials for "Uncertainty-aware Construction Task Scheduling for Human-robot Collaboration via Fuzzy-time Model and Deep Reinforcement Learning"

This record contains supplemental materials associated with the manuscript entitled “Uncertainty-aware Construction Task Scheduling for Human-robot Collaboration via Fuzzy-time Model and Deep Reinforcement Learning.” The supplemental materials provide extended figures and experimental results supporting the analyses pr...

OPTICAT: A Deep Reinforcement Learning Framework for Instance-Specific Algorithm Configuration

Optimization algorithms contain parameters that greatly influence their behavior. Finding the right settings for parameters through automated algorithm configuration has become a critical component of designing competitive algorithms. While traditional offline configurators tackle this problem by finding one configurat...

THE ROLE OF ARTIFICIAL INTELLIGENCE IN DIABETES CARE AND MANAGEMENT: A COMPREHENSIVE REVIEW

Diabetes mellitus represents a global metabolic crisis characterized by complex self-management requirements, marked physiological variability, and significant risks of microvascular and macrovascular complications. The integration of Artificial Intelligence (AI)—including Machine Learning (ML), Deep Learning (DL), Nat...