37 citations · 142 across the 32 of their papers we have counts for
6 papers · 1 filter
Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study
Julian F. Schumann, Aravinda Ramakrishnan Srinivasan, Jens Kober +2
The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable m…
DeepKoCo: Efficient latent planning with a task-relevant Koopman representation
Bas van der Heijden, Laura Ferranti, Jens Kober +1
This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images. This representation allows DeepKoCo to plan efficiently using linea…
Smooth Exploration for Robotic Reinforcement Learning
Antonin Raffin, Jens Kober, Freek Stulp
Reinforcement learning (RL) enables robots to learn skills from interactions with the real world. In practice, the unstructured step-based exploration used in Deep RL -- often very…
Deep Reinforcement Learning with Feedback-based Exploration
Jan Scholten, Daan Wout, Carlos Celemin +1
Deep Reinforcement Learning has enabled the control of increasingly complex and high-dimensional problems. However, the need of vast amounts of data before reasonable performance i…
Learning Gaussian Policies from Corrective Human Feedback
Daan Wout, Jan Scholten, Carlos Celemin +1
Learning from human feedback is a viable alternative to control design that does not require modelling or control expertise. Particularly, learning from corrective advice garners a…
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks
Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar +1
Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs). However, it is highly data demandin…