319 citations · 614 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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,…