Optimal energy system scheduling using a constraint-aware reinforcement learning algorithm

The massive integration of renewable-based distributed energy resources (DERs) inherently increases the energy system’s complexity, especially when it comes to defining its operational schedule. Deep reinforcement learning (DRL) algorithms arise as a promising solution due to their data-driven and model-free features. However, current DRL...

Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes

This paper proposes a method to encourage safety in Model Predictive Control (MPC)-based Reinforcement Learning (RL) via Gaussian Process (GP) regression. The framework consists of 1) a parametric MPC scheme that is employed as model-based controller with approximate knowledge on the real system's dynamics, 2) an episodic RL algorithm tasked...

Safe Reinforcement Learning for Automated Vehicles

Fully automated vehicles have the potential to increase road safety and improve traffic flow by taking the human element out of the driving loop. They can also provide mobility to people who are unable to operate a conventional vehicle. Safe automated vehicles must be able to respond in emergency situations or drive on slippery roads in bad...

Safe Optimization of Steel Manufacturing with Reinforcement Learning

Steel production is a complex problem, and little has been done to improve it with the usage of Reinforcement Learning techniques. Most studies focus on decomposing it into sub-problems, instead of tacking it as a whole. Research has shown promising results in the area of safe policy improvement on toy problems. These algorithms are not only...

Safe reinforcement learning in long-horizon partially observable environments

Deep reinforcement learning went through an unprecedented development in the last decade, resulting in agents defeating world champion human players in complex board games like go and chess. With few exceptions, deep reinforcement learning research focuses on fully observable environments, while there is slightly less research in the direction...