319 citations · 614 across the 14 of their papers we have counts for
4 papers · 1 filter
Safe Exploration for Interactive Machine Learning
Matteo Turchetta, Felix Berkenkamp, Andreas Krause
In Interactive Machine Learning (IML), we iteratively make decisions and obtain noisy observations of an unknown function. While IML methods, e.g., Bayesian optimization and active…
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
Matteo Turchetta, Andreas Krause, Sebastian Trimpe
In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by maximizing an exogenous reward signal while interacting with its environment. In real-world a…
Mixed-Variable Bayesian Optimization
Erik Daxberger, Anastasia Makarova, Matteo Turchetta +1
The optimization of expensive to evaluate, black-box, mixed-variable functions, i.e. functions that have continuous and discrete inputs, is a difficult and yet pervasive problem in…
Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning
Torsten Koller, Felix Berkenkamp, Matteo Turchetta +2
Reinforcement learning has been successfully used to solve difficult tasks in complex unknown environments. However, these methods typically do not provide any safety guarantees du…