52 citations · 81 across the 4 of their papers we have counts for
9 papers
Near-Optimal Multi-Agent Learning for Safe Coverage Control
Manish Prajapat, Matteo Turchetta, Melanie N. Zeilinger +1
In multi-agent coverage control problems, agents navigate their environment to reach locations that maximize the coverage of some density. In practice, the density is rarely known…
GoSafe: Globally Optimal Safe Robot Learning
Dominik Baumann, Alonso Marco, Matteo Turchetta +1
When learning policies for robotic systems from data, safety is a major concern, as violation of safety constraints may cause hardware damage. SafeOpt is an efficient Bayesian opti…
Safe and Efficient Model-free Adaptive Control via Bayesian Optimization
Christopher König, Matteo Turchetta, John Lygeros +2
Adaptive control approaches yield high-performance controllers when a precise system model or suitable parametrizations of the controller are available. Existing data-driven approa…
Safe Reinforcement Learning via Curriculum Induction
Matteo Turchetta, Andrey Kolobov, Shital Shah +2
In safety-critical applications, autonomous agents may need to learn in an environment where mistakes can be very costly. In such settings, the agent needs to behave safely not onl…
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…