5 citations · 9 across the 5 of their papers we have counts for
3 papers · 1 filter
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach
Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar +1
Deep Reinforcement Learning (DRL) has become a powerful methodology to solve complex decision-making problems. However, DRL has several limitations when used in real-world problems…
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…