A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Interactive Learning with Corrective Feedback for Policies Based on Deep Neural Networks
[chapter]
2020
Distributed Autonomous Robotic Systems
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 demanding, so unfeasible in physical systems for most applications. In this work, we approach an alternative Interactive Machine Learning (IML) strategy for training DNN policies based on human corrective feedback, with a method called Deep COACH (D-COACH). This approach not only takes advantage of the knowledge and insights
doi:10.1007/978-3-030-33950-0_31
fatcat:icyg3pv7vzgnjgop6jw3luapie