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- Title
A reinforcement learning-based scheme for direct adaptive optimal control of linear stochastic systems.
- Authors
Wong, Wee Chin; Lee, Jay H.
- Abstract
Reinforcement learning where decision-making agents learn optimal policies through environmental interactions is an attractive paradigm for model-free, adaptive controller design. However, results for systems with continuous state and action variables are rare. In this paper, we present convergence results for optimal linear quadratic control of discrete-time linear stochastic systems. This work can be viewed as a generalization of a previous work on deterministic linear systems. Key differences between the algorithms for deterministic and stochastic systems are highlighted through examples. The usefulness of the algorithm is demonstrated through a nonlinear chemostat bioreactor case study. Copyright © 2009 John Wiley & Sons, Ltd.
- Publication
Optimal Control - Applications & Methods, 2010, Vol 31, Issue 4, p365
- ISSN
0143-2087
- Publication type
Article
- DOI
10.1002/oca.915