Machine learning frameworks such as genetic programming and reinforcement learning
(RL) are gaining popularity in flow control. This work presents a comparative analysis of the …

Fighting wildfires is a precarious task, imperiling the lives of engaging firefighters and those
who reside in the fire's path. Firefighters need online and dynamic observation of the firefront …

The commonly adopted route to control a dynamic system and make it follow the desired
behavior consists of two steps. First, a model of the system is learned from input–output data …

In this work, we systematically integrate relevant expertise, specifically on the optimal brake-
specific fuel consumption (BSFC) curve, battery characteristics and terrain information into …

Policy gradient (PG) algorithms have been widely used in reinforcement learning (RL).
However, PG algorithms rely on exploiting the value function being learned with the first …

High-performing human teams leverage intelligent and efficient communication and coordi-
nation strategies to collaboratively maximize their joint utility. Inspired by teaming behaviors …

In this thesis I focus on the applications and relevance of orthogonality in various topics in
machine learning. The theme of the thesis is that different viewpoints of the concept of …

This article reviews some kernel-based approaches for system identification and learning-
based control. In the first part, the presentation moves from classic linear system …

Despite the remarkable progress made by the policy gradient algorithms in reinforcement
learning (RL), sub-optimal policies usually result from the local exploration property of the …