Carlos Celemin

Deep Reinforcement Learning with Feedback-based Exploration

2019

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 is attained prevents its widespread application. We employ binary corrective feedback …

Simultaneous Learning of Objective Function and Policy from Interactive Teaching with Corrective Feedback

2019

Some imitation learning approaches rely on Inverse Reinforcement Learning (IRL) methods, to decode and generalize implicit goals given by expert demonstrations. The study of IRL normally has the assumption of available expert demonstrations, which is …