Actor-critic reinforcement learning for tracking control in robotics

Conference Paper (2016)
Author(s)

Yudha P. Pane (Katholieke Universiteit Leuven)

SP Nageshrao (TU Delft - OLD Intelligent Control & Robotics)

R. Babuska (TU Delft - OLD Intelligent Control & Robotics)

Research Group
OLD Intelligent Control & Robotics
DOI related publication
https://doi.org/10.1109/CDC.2016.7799164
More Info
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Publication Year
2016
Language
English
Research Group
OLD Intelligent Control & Robotics
Pages (from-to)
5819-5826
ISBN (electronic)
978-1-5090-1837-6

Abstract

In this article we provide experimental results and evaluation of a compensation method which improves the tracking performance of a nominal feedback controller by means of reinforcement learning (RL). The compensator is based on the actor-critic scheme and it adds a correction signal to the nominal control input with the goal to improve the tracking performance using on-line learning. The algorithm has been evaluated on a 6 DOF industrial robot manipulator with the objective to accurately track different types of reference trajectories. An extensive experimental study has shown that the proposed RL-based compensation method significantly improves the performance of the nominal feedback controller.

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