Reference
L. Buşoniu, B. De Schutter, and R. Babuška, "Decentralized
reinforcement learning control of a robotic manipulator,"
Proceedings of the 9th International Conference on Control,
Automation, Robotics and Vision (ICARCV 2006), Singapore, pp. 1347-1352,
Dec. 2006.
Abstract
Multi-agent systems are rapidly finding applications in a variety of domains,
including robotics, distributed control, telecommunications, etc. Learning
approaches to multi-agent control, many of them based on reinforcement learning
(RL), are investigated in complex domains such as teams of mobile robots.
However, the application of decentralized RL to low-level control tasks is not
as intensively studied. In this paper, we investigate centralized and
decentralized RL, emphasizing the challenges and potential advantages of the
latter. These are then illustrated on an example: learning to control a
two-link rigid manipulator. Some open issues and future research directions in
decentralized RL are outlined.
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BibTeX
@inproceedings{BusDeS:06-026,
author = {Bu{\c{s}}oniu, Lucian and De Schutter, Bart and Babu{\v{s}}ka,
Robert},
title = {Decentralized Reinforcement Learning Control of a Robotic
Manipulator},
booktitle = {Proceedings of the 9th International Conference on Control,
Automation, Robotics and Vision (ICARCV 2006)},
address = {Singapore},
pages = {1347--1352},
month = dec,
year = {2006}
}