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A reinforcement learning-based scheme for adaptive optimal control of linear stochastic systems

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2 Author(s)
Wee Chin Wong ; Dept. of Chem. & Biomol. Eng., Georgia Inst. of Technol., Atlanta, GA ; Lee, J.H.

Reinforcement learning where decision-making agents learn optimal policies through environmental interactions is an attractive paradigm for direct, adaptive controller design. However, results for systems with continuous variables are rare. Here, we generalize a previous work on deterministic linear systems, to stochastic ones, since uncertainty is almost always present and needs to be accounted for to ensure good closed-loop performance. In this work, we present convergence results and also show an example suggesting automatic controller order-reduction. We also highlight key differences between the algorithms for deterministic and stochastic systems.

Published in:

American Control Conference, 2008

Date of Conference:

11-13 June 2008

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