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pro vyhledávání: '"Airaldi, Filippo"'
Unconstrained global optimisation aims to optimise expensive-to-evaluate black-box functions without gradient information. Bayesian optimisation, one of the most well-known techniques, typically employs Gaussian processes as surrogate models, leverag
Externí odkaz:
http://arxiv.org/abs/2412.04882
Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while
Externí odkaz:
http://arxiv.org/abs/2409.12789
This paper presents a novel approach to multi-agent reinforcement learning (RL) for linear systems with convex polytopic constraints. Existing work on RL has demonstrated the use of model predictive control (MPC) as a function approximator for the po
Externí odkaz:
http://arxiv.org/abs/2312.05166
In the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strategies to enhance traffic flow management by use of an innovative appro
Externí odkaz:
http://arxiv.org/abs/2311.08820
Publikováno v:
IFAC-PapersOnLine, 56 (2), 2023, 5759-5764
We propose a method to encourage safety in Model Predictive Control (MPC)-based Reinforcement Learning (RL) via Gaussian Process (GP) regression. This framework consists of 1) a parametric MPC scheme that is employed as model-based controller with ap
Externí odkaz:
http://arxiv.org/abs/2211.01860
Publikováno v:
In IFAC PapersOnLine 2023 56(2):5759-5764
Akademický článek
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Publikováno v:
In Automatica September 2024 167