Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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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.