319 citations · 614 across the 14 of their papers we have counts for
16 papers
Breeding Programs Optimization with Reinforcement Learning
Omar G. Younis, Luca Corinzia, Ioannis N. Athanasiadis +3
Crop breeding is crucial in improving agricultural productivity while potentially decreasing land usage, greenhouse gas emissions, and water consumption. However, breeding programs…
Safe Guaranteed Exploration for Non-linear Systems
Manish Prajapat, Johannes Köhler, Matteo Turchetta +2
Safely exploring environments with a-priori unknown constraints is a fundamental challenge that restricts the autonomy of robots. While safety is paramount, guarantees on sufficien…
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…
GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems
Bhavya Sukhija, Matteo Turchetta, David Lindner +3
Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing model-free learning meth…
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…
Information Directed Reward Learning for Reinforcement Learning
David Lindner, Matteo Turchetta, Sebastian Tschiatschek +2
For many reinforcement learning (RL) applications, specifying a reward is difficult. This paper considers an RL setting where the agent obtains information about the reward only by…
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…
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…
Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations
Mark Pfeiffer, Samarth Shukla, Matteo Turchetta +4
This work presents a case study of a learning-based approach for target driven map-less navigation. The underlying navigation model is an end-to-end neural network which is trained…
Learning-based Model Predictive Control for Safe Exploration
Torsten Koller, Felix Berkenkamp, Matteo Turchetta +1
Learning-based methods have been successful in solving complex control tasks without significant prior knowledge about the system. However, these methods typically do not provide a…
Safe Model-based Reinforcement Learning with Stability Guarantees
Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig +1
Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. However, to find optimal policies, most reinforcement learning algorithms explor…
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
Matteo Turchetta, Felix Berkenkamp, Andreas Krause
In classical reinforcement learning, when exploring an environment, agents accept arbitrary short term loss for long term gain. This is infeasible for safety critical applications,…