reinforcement learning

racetraQ: a quantum reinforcement learning racing demo

A race car driven by a variational quantum circuit trained with double deep Q-learning, next to a matched classical baseline, with multi-seed studies, a light-cone analysis and a hardware path to IBM Quantum devices.

Tail-Risk-Aware Model Predictive Control for Hypoglycemia Mitigation in Automated Insulin Delivery

Background: Automated insulin delivery must maintain glycemic control despite partial observability, physiological heterogeneity, and sensing and actuation errors. Cohort-average metrics may nevertheless conceal hypoglycemia in vulnerable virtual patients. Methods: We developed a tail-risk-aware model predictive contro...

Reward-Quality Divergence (RQD): Code, Saved Runs and Analysis Scripts (Anonymous Review Artifact)

Anonymous review artifact for the paper Reward-Quality Divergence: Telling Judge Gaming from Judge Error in Visual RL. Please do not try to identify the authors. RQD measures the gap between a judge's reward and an anchor-based quality score during reinforcement learning, subtracts the gap that exists before training (...

Multi-Level Deep Deterministic Policy Gradient reinforcement learning for safety-constrained trajectory optimization of collaborative industrial robots

Collaborative robots operating in modern manufacturing environments require trajectory planning strategies that simultaneously support efficiency, adaptability, and operational safety. This study proposes a Multi-Level Deep Deterministic Policy Gradient (ML-DDPG) framework for safety-constrained trajectory optimization...

A Systematic Survey of Smart Contract Fuzzing : Methods, Techniques, and Architectures for Ethereum and Beyond

Blockchain architectures increasingly rely on smart contracts as programmable execution components, yet the security challenges they introduce remain only partially addressed. Fuzz testing has emerged as one of the leading automated techniques for smart contract vulnerability discovery; however, a systematic treatment...

Fire Risk Intervention for Lithium‑Ion Batteries During Air Transport Based on Reinforcement Learning

Collaborative optimization of electricity, computation, and thermal resources in data centers: Progress, challenges, and future directions

Data centers increasingly rely on coordinated operation across power, computation, and thermal management resources, but existing studies differ substantially in the resources coordinated, decision methods adopted, and evidence supporting their conclusions. This literature review synthesizes 311 unique studies spanning...

A Dynamic Graph-Based Autonomous Coordination for Enhancing Cockpit Intelligence

Simulation Study of 3D UAV Path Planning Using Proximal Policy Optimization Reinforcement Learning

Implementation and Performance Evaluation of a Solar-Powered Closed-Loop Recirculating Hydro-Gravitational Energy Storage System with Reinforcement Learning Control

This repository contains dataset files, mathematical model implementations, and code artifacts for a Solar-Powered Closed-Loop Recirculating Water Gravity Battery System. The system integrates a differentiable digital twin framework for microgrid energy management, deep reinforcement learning algorithms (including Prox...

Consider stock indices and technical indicators to conduct price forecasting for gold and oil: a reinforcement learning perspective

Rising oil prices often trigger inflation, whereas higher gold prices reflect risk aversion. Analyzing daily data from 2011 to 2025, this study proposes a novel framework to predict commodity prices: (1) raw materials, stock indices, and technical indicators are integrated as the predictors, (2) key performance indic...

DEVELOPING AUTONOMOUS CYBER DEFENCE SYSTEMS USING AI-DRIVEN INTRUSION DETECTION FOR IOT NETWORKS

As the Internet of Things (IoT) continues to grow exponentially, securing interconnected devices against evolving cyber threats has become paramount. This paper explores the development of an autonomous cyber defence system leveraging artificial intelligence (AI) to enhance intrusion detection for IoT networks. Traditi...