Quantum computing will not deliver truly useful business results overnight, but the pace of progress is no longer linear; ...

This paper presents the hierarchical Q-learning path planning (HQPP) architecture for solving the cooperative tracking control problem of multi-agent systems (MASs) with lumped uncertainties in an ...

The strategic advantage of QML continues to expand its presence in industries that deal with complex, high-dimensional data.

The deadline is closing in, compiler errors keep flashing across your screen, and the fear of lost GPA points feels like a ...

A multi-objective adaptive traffic signal control algorithm using fuzzy control and Q-learning was proposed to improve the efficiency, traffic safety, and operational stability of signalized ...

A modular, high-level Python package for Deep Reinforcement Learning, designed to simplify the implementation and study of DRL algorithms, offering an accessible and extensible framework for students, ...

This study introduces a Q-learning-based nonlinear model predictive control (QL-NMPC) framework for temperature control in batch reactors. A reinforcement learning agent is trained in simulation to ...

This project implements a Reinforcement Learning agent that learns to play the classic game of Nim using Q-learning, one of the foundational algorithms in model-free Reinforcement Learning (RL).