Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning

31 May 2022

Papers citing "Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning"

8 / 8 papers shown

Title
Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control L. D. Natale B. Svetozarevic Philipp Heer Colin N. Jones OffRL AI4CE 66 6 0 10 Mar 2022
Physically Consistent Neural Networks for building thermal modeling: theory and analysis L. D. Natale B. Svetozarevic Philipp Heer Colin N. Jones PINN AI4CE 84 88 0 06 Dec 2021
Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments B. Svetozarevic C. Baumann S. Muntwiler L. Di Natale Melanie Zeilinger Philipp Heer AI4CE 33 35 0 02 Mar 2021
Data-Driven MPC for Quadrotors G. Torrente Elia Kaufmann Philip Föhn Davide Scaramuzza 125 210 0 10 Feb 2021
How Training Data Impacts Performance in Learning-based Control Armin Lederer A. Capone Jonas Umlauft Sandra Hirche 53 18 0 25 May 2020
Localized active learning of Gaussian process state space models A. Capone Jonas Umlauft Thomas Beckers Armin Lederer Sandra Hirche 38 28 0 04 May 2020
Addressing Function Approximation Error in Actor-Critic Methods Scott Fujimoto H. V. Hoof David Meger OffRL 183 5,212 0 26 Feb 2018
Identification of Gaussian Process State Space Models Stefanos Eleftheriadis Tom Nicholson M. Deisenroth J. Hensman 68 113 0 30 May 2017