A tutorial on derivative-free policy learning methods for interpretable controller representations
This paper provides a tutorial overview of recent advances in learning control policy
representations for complex systems. We focus on control policies that are determined by …
representations for complex systems. We focus on control policies that are determined by …
No-regret Bayesian optimization with unknown equality and inequality constraints using exact penalty functions
Bayesian optimization (BO) methods have been successfully applied to many challenging
black-box optimization problems involving expensive-to-evaluate functions. Although BO is …
black-box optimization problems involving expensive-to-evaluate functions. Although BO is …
Gradient-enhanced Bayesian optimization via acquisition ensembles with application to reinforcement learning
Bayesian optimization (BO) has shown great promise as a data-efficient strategy for the
global optimization of expensive, black-box functions in a plethora of control applications …
global optimization of expensive, black-box functions in a plethora of control applications …
Surrogate indirect adaptive controller tuning based on polynomial response surface method and bioinspired optimization: Application to the brushless direct current …
The increment of autonomous systems has stimulated the research of new controller tuning
techniques to face the unpredictable disturbances and parametric uncertainties inherent in …
techniques to face the unpredictable disturbances and parametric uncertainties inherent in …
[HTML][HTML] Personalization of Robot Behavior Using Approach Based on Model Predictive Control
M Jarosz, B Sniezynski - Applied Sciences, 2024 - mdpi.com
This paper proposes a novel approach to personalizing robot behavior using Model
Predictive Control (MPC). Social humanoid robots, equipped with advanced sensors and …
Predictive Control (MPC). Social humanoid robots, equipped with advanced sensors and …
Multi-agent black-box optimization using a Bayesian approach to alternating direction method of multipliers
Bayesian optimization (BO) is a powerful black-box optimization framework that looks to
efficiently learn the global optimum of an unknown system by systematically trading-off …
efficiently learn the global optimum of an unknown system by systematically trading-off …
Advances in Constrained Bayesian Optimization for Complex Grey-Box Systems: Theory, Methods, and Applications
C Lu - 2024 - search.proquest.com
Optimization plays a critical role in the chemical engineering industry due to its potential to
improve efficiency, safety, sustainability, and profitability in various processes. While modern …
improve efficiency, safety, sustainability, and profitability in various processes. While modern …
[BOOK][B] Data-Driven Learning and Optimization of Dynamical Systems
G Makrygiorgos - 2023 - search.proquest.com
Dynamical systems analysis and optimization is pivotal for safe, efficient, and resilient
processes that consistently deliver high-quality products. Conventionally, decision-making …
processes that consistently deliver high-quality products. Conventionally, decision-making …