Adaptive linear quadratic control using policy iteration
Top Cited Papers
- 24 August 2005
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 3, 3475-3479
- https://doi.org/10.1109/acc.1994.735224
Abstract
In this paper we present stability and convergence results for Dynamic Programming-based reinforcement learning applied to Linear Quadratic Regulation (LQR). The specific algorithm we analyze is based on Q-learning and it is proven to converge to the optimal controller provided that the underlying system is controllable and a particular signal vector is persistently excited. The performance of the algorithm is illustrated by applying it to a model of a flexible beam.Keywords
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