reinforcement learning

Assembling Two Parts in One Hand

A reinforcement learning formulation is presented to solve in-hand assembly: mating two rigid objects within a single dexterous hand, with no second arm and no fixture, which shows robustness to state-estimation errors caused by oc- clusion.

A Risk-Sensitive and Uncertainty-Aware Decision-Making and Control Framework for Safe and Robust Autonomous Driving

A Risk-sensitive and Uncertainty-aware Decision-making and Control (RUDC) framework for safe and robust autonomous driving that couples risk-sensitive distributional RL with ensemble-based policy uncertainty quantification, jointly accounting for tail risks in return distributions and uncertainty in learned policies.

Physics-informed reinforcement learning for energy-efficient unmanned aerial vehicle navigation with context-aware power estimation

Model-assisted multi-agent reinforcement learning for collaborative synchromodal transport re-planning under service time uncertainty

Earnings forecasting by LLMs enhanced via reinforcement learning with synthetic financial reports

A novel heuristic deep reinforcement learning model for deformation online control in thin-walled part milling

A reinforcement learning-based spatiotemporal dynamic multi-graph ensemble framework for multi-station air quality prediction

An evolutionary-guided deep reinforcement learning approach to detect influential nodes from networked systems

An automatic parameter search framework for agricultural machinery trajectory operation mode identification by deeply weakly informed deep reinforcement learning

Digital twin-driven deep reinforcement learning for coordinated scheduling and state prediction of distributed energy storage clusters

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...

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...