1 code implementation • 27 Aug 2022 • Nicolò Botteghi, Mannes Poel, Christoph Brune
This review addresses the problem of learning abstract representations of the measurement data in the context of Deep Reinforcement Learning (DRL).
no code implementations • 8 Jul 2021 • Nicolò Botteghi, Luuk Grefte, Mannes Poel, Beril Sirmacek, Christoph Brune, Edwin Dertien, Stefano Stramigioli
Inspection and maintenance are two crucial aspects of industrial pipeline plants.
Autonomous Navigation Hierarchical Reinforcement Learning +2
no code implementations • 4 Jul 2021 • Nicolò Botteghi, Mannes Poel, Beril Sirmacek, Christoph Brune
Results show that the novel framework can efficiently learn low-dimensional and interpretable state and action representations and the optimal latent policy.
no code implementations • 4 Jul 2021 • Nicolò Botteghi, Khaled Alaa, Mannes Poel, Beril Sirmacek, Christoph Brune, Abeje Mersha, Stefano Stramigioli
Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life.
no code implementations • 29 Jul 2020 • Nicolò Botteghi, Ruben Obbink, Daan Geijs, Mannes Poel, Beril Sirmacek, Christoph Brune, Abeje Mersha, Stefano Stramigioli
We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space.
no code implementations • 10 Feb 2020 • Nicolò Botteghi, Beril Sirmacek, Khaled A. A. Mustafa, Mannes Poel, Stefano Stramigioli
We present a map-less path planning algorithm based on Deep Reinforcement Learning (DRL) for mobile robots navigating in unknown environment that only relies on 40-dimensional raw laser data and odometry information.