This research project investigates the application of Deep Reinforcement Learning (DRL) for active flow control around a confined square cylinder. By combining Computational Fluid Dynamics (CFD) ...

Such agents are built with the help of a paradigm of machine learning called “Reinforcement Learning” (RL). In this course, you’ll walk through different approaches to RL. You’ll move from a simple ...

In this study, we propose a real-time contrasts control chart based on reinforcement learning (RL-RTC). Effective process monitoring, which directly influences productivity and yield, has become ...

The complexity and uncertainty in power systems leads to a great challenge for controlling the power grid using traditional manual adjustment methods. Reinforcement learning is a promising data-driven ...

Reinforcement learning (RL) is a branch of machine learning (ML) that enables agents to learn from their own actions and rewards in an environment. However, RL often faces challenges such as high ...

Reinforcement learning (RL) is a branch of machine learning that focuses on learning from trial and error, rather than from supervised or unsupervised data. RL agents interact with an environment ...