In the past couple of years, the interest of the fluid mechanics community for deep
reinforcement learning techniques has increased at fast pace, leading to a growing …

This research gauges the ability of deep reinforcement learning (DRL) techniques to assist
the control of conjugate heat transfer systems governed by the coupled Navier–Stokes and …

This paper proposes a Deep Reinforcement Learning approach for optimally managing
multi-energy systems in smart grids. The optimal control problem of the production and …

Achieving efficiency gains in Chinese district heating networks, thereby reducing their
carbon footprint, requires new optimal control methods going beyond current industry tools …

Agents, whether software or hardware, perceive their environment through sensors and act
using actuators, often operating in dynamic, partially observable settings. They face …

This research gauges the ability of deep reinforcement learning (DRL) techniques to assist
the control of conjugate heat transfer systems governed by the coupled Navier--Stokes and …

This research introduces a Deep Reinforcement Learning (DRL)-based approach for the
optimized energy management in Smart Multi-Energy Systems (SMES). A Smart Energy …

This thesis gauges the relevance of deep reinforcement learning (DRL) techniques for the
optimal control of fluid mechanical systems. Reinforcement learning (RL) is the process by …