ICONS 2019 Paper Abstract


Paper ThB2SP.1

Görges, Daniel (University of Kaiserslautern)

Distributed Adaptive Linear Quadratic Control Using Distributed Reinforcement Learning

Scheduled for presentation during the Regular Session "Learning and Control" (ThB2SP), Thursday, August 22, 2019, 16:00−16:20,

5th IFAC International Conference on Intelligent Control and Automation Sciences, August 21-23, 2019, Queen’s University Belfast, Northern Ireland

This information is tentative and subject to change. Compiled on November 29, 2021

Keywords Reinforcement learning, Learning, adaptation and evaluation, Controllers and observers design


In this paper distributed adaptive linear quadratic control of discrete-time linear large-scale systems with unknown dynamics using distributed reinforcement learning is studied. Linear quadratic control based on dynamic programming (specifically policy iteration) and adaptive linear quadratic control based on reinforcement learning (especially Q learning) are reviewed first. Then distributed adaptive linear quadratic control is addressed. Two Q functions exploiting the quadratic structure of the value function and leading to a decentralized and a distributed policy are proposed and a decentralized as well as a distributed Q learning algorithm are presented. Finally the concepts are evaluated in a simulation study. The simulation results indicate that the distributed policy is near-optimal.


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