reinforcement learning

Verification Is Not Validity: From Self-Verifiable Rewards to Governed Execution

A control-plane analysis distinguishing three concepts routinely collapsed in contemporary AI discourse: event verifiability, proxy validity, and governance sufficiency. Taking Reinforcement Learning with Self-Verifiable Rewards and its SpyRL instantiation as its case, it accepts the engineering contribution — an envir...

Machine learning-based framework for real-time seismic damage assessment of RC buildings in Nepal

Seismic fragility curves help assess structural damage based on a few structural and ground motion (GM) features. In Nepal, diverse construction practices exist, and current standards require reinforced concrete (RC) buildings to ensure structural integrity under design spectra for different soil types. To study this...

Reinforcement-learning-based adaptive energy management strategy for microgrid energy storage systems

High renewable penetration makes microgrid energy management sensitive to uncertain photovoltaic output, wind fluctuation, load variation, electricity price, and battery degradation. Conventional rule-based and model predictive strategies require manually tuned thresholds or accurate forecasts, which limits their adapt...

Recent Advances of Reinforcement Learning Algorithms for Autonomous Driving System

This survey examines RL-based AD in modular and end-to-end pipelines and relates reported methods to task formulation and deployment evidence and examines deployment barriers, including safety, Sim2Real generalization, data efficiency, computation, embodied alignment, and evaluation readiness.

arjyadebsengupta-netizen/Julia-based-SciML-and-Reinforcement-Learning-Applications: Julia Based ML and RL

Full Changelog: https://github.com/arjyadebsengupta-netizen/Julia-based-SciML-and-Reinforcement-Learning-Applications/commits/v.0.0

Geometry-guided and interaction-aware multi-agent reinforcement learning for cooperative multi-USV encirclement

MineAgent: Deep Reinforcement Learning-based Testing for Minecraft

This package contains the extended test results for my Bachelor thesis MineAgent: Deep Reinforcement Learning-based Testing for Minecraft at the University of Trento, supervised by Fondazione Bruno Kessler (FBK) researchers.

Enhancing Sim-to-Real Transfer for a High-Gear-Ratio Quadruped Robot via Extended Actuator Dynamics Identification

An augmented friction model that incorporates the Stribeck effect to capture the low-velocity nonlinearities characteristic of high-gear-ratio actuators and indicates that explicitly modeling low-velocity friction is beneficial for high-fidelity sim-to-real transfer in high-reduction systems.

Source-Study-Aware Validation and Explainable Machine Learning for Reliable Shear Capacity Assessment of Steel-Fiber-Reinforced Concrete Corbels

This study proposes a source-study-aware validation framework to estimate the shear capacity of stirrup-free steel-fiber-reinforced concrete corbels and assess the reliability and generalizability of machine-learning models in structural engineering. A database of 108 specimens, compiled from seven independent studies,...

The Role of Teachers in Helping Students Overcome Learning Difficulties in Class X History at MA Negeri Insan Cendekia Palu City

This study aims to identify the factors causing students' learning difficulties in History and to describe the teacher's role in helping tenth-grade students overcome these learning difficulties at MA Negeri Insan Cendekia Palu. This research employed a descriptive qualitative approach. The participants consisted of th...

Stochastic Procrastination and Optimal Task Division

Procrastination is widespread, costly, and persistent despite strong intentions to change. We show how procrastination can arise in stationary environments, where there are no deadlines, changing incentives, or new information to justify delay. With hyperbolic discounting, stochastic delay, i.e., acting with a fixed pr...

Context-Aware Sequential Ranking for Dynamic Test Case Prioritization in Continuous Integration

Regression testing in continuous integration (CI) helps preserve delivered software value by exposing regressions early, but limited feedback windows make test order consequential. This study evaluates a leakage-safe dynamic test case prioritization framework that reconstructs job–build–commit provenance, combines stri...