Searched for: author%3A%22Kober%2C+J.%22
(1 - 5 of 5)
document
Pérez-Dattari, Rodrigo (author), Ferreira de Brito, B.F. (author), de Groot, O.M. (author), Kober, J. (author), Alonso-Mora, J. (author)
The successful integration of autonomous robots in real-world environments strongly depends on their ability to reason from context and take socially acceptable actions. Current autonomous navigation systems mainly rely on geometric information and hard-coded rules to induce safe and socially compliant behaviors. Yet, in unstructured urban...
journal article 2022
document
Bonsignorio, Fabio (author), Hsu, David (author), Johnson-Roberson, Matthew (author), Kober, J. (author)
Deep learning has gone through massive growth in recent years. In many fields—computer vision, speech recognition, machine translation, game playing, and others—deep learning has brought unprecedented progress and become the method of choice. Will the same happen in robotics and automation? In a sense, it is already happening. Today, deep...
contribution to periodical 2020
document
de Bruin, T.D. (author), Kober, J. (author), Tuyls, Karl (author), Babuska, R. (author)
Deep reinforcement learning makes it possible to train control policies that map high-dimensional observations to actions. These methods typically use gradient-based optimization techniques to enable relatively efficient learning, but are notoriously sensitive to hyperparameter choices and do not have good convergence properties. Gradient...
journal article 2020
document
de Bruin, T.D. (author), Kober, J. (author), Tuyls, K.P. (author), Babuska, R. (author)
Experience replay is a technique that allows off-policy reinforcement-learning methods to reuse past experiences. The stability and speed of convergence of reinforcement learning, as well as the eventual performance of the learned policy, are strongly dependent on the experiences being replayed. Which experiences are replayed depends on two...
journal article 2018
document
Buşoniu, Lucian (author), de Bruin, T.D. (author), Tolić, Domagoj (author), Kober, J. (author), Palunko, Ivana (author)
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain. This review mainly covers artificial-intelligence approaches to RL, from the viewpoint of the control engineer. We explain how approximate representations of the...
review 2018
Searched for: author%3A%22Kober%2C+J.%22
(1 - 5 of 5)