Search Results for author:
Found 15 papers, 6 papers with code
Principled Preferential Bayesian Optimization
We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions.
Stable Linear Subspace Identification: A Machine Learning Approach
Machine Learning (ML) and linear System Identification (SI) have been historically developed independently.
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints
Additionally, the algorithm guarantees an $\mathcal{O}(N\sqrt{T})$ bound on the cumulative violation for the known affine constraints, where $N$ is the number of agents.
Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approach
We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal discomfort of the occupants is below a given threshold.
Bayesian Optimization of Expensive Nested Grey-Box Functions
We consider the problem of optimizing a grey-box objective function, i. e., nested function composed of both black-box and white-box functions.
Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints
This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances.
Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics.
Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Models
While physically sound, classical gray-box models are often cumbersome to identify and scale, and their accuracy might be hindered by their limited expressiveness.
Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules
Model-free Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing policies.
CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints
In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints.
Physically Consistent Neural ODEs for Learning Multi-Physics Systems
Since IPHS models are consistent with the first and second principles of thermodynamics by design, so are the proposed Physically Consistent NODEs (PC-NODEs).
Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control
Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector.
Physically Consistent Neural Networks for building thermal modeling: theory and analysis
To counter this known generalization issue, physics-informed NNs have recently been introduced, where researchers introduce prior knowledge in the structure of NNs to ground them in known underlying physical laws and avoid classical NN generalization issues.
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics.
Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies
Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them.
