118 citations · 570 across the 100 of their papers we have counts for
7 papers · 1 filter
Foundations of Reinforcement Learning and Control:Connections and New Perspectives
Claire Vernade, Onno Eberhard, Martha White +4
Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields…
A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control
Gennaro Guidone, Luca Monegaglia, Elia Raimondi +3
We present a novel decentralized algorithm for coverage control in unknown spatial environments modeled by Gaussian Processes (GPs). To trade-off between exploration and exploitati…
Learning diffusion at lightspeed
Antonio Terpin, Nicolas Lanzetti, Martin Gadea +1
Diffusion regulates numerous natural processes and the dynamics of many successful generative models. Existing models to learn the diffusion terms from observational data rely on c…
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL
Lenart Treven, Bhavya Sukhija, Yarden As +2
Reinforcement learning (RL) excels in optimizing policies for discrete-time Markov decision processes (MDP). However, various systems are inherently continuous in time, making disc…
Efficient Exploration in Continuous-time Model-based Reinforcement Learning
Lenart Treven, Jonas Hübotter, Bhavya Sukhija +2
Reinforcement learning algorithms typically consider discrete-time dynamics, even though the underlying systems are often continuous in time. In this paper, we introduce a model-ba…
Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems
Jules Authier, Rabab Haider, Anuradha Annaswamy +1
To maintain a reliable grid we need fast decision-making algorithms for complex problems like Dynamic Reconfiguration (DyR). DyR optimizes distribution grid switch settings in real…