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In this paper, a novel discrete-time deterministic $ Q$ -learning algorithm is developed. In each iteration of the developed $ Q$ -learning algorithm, the iterative $ Q$ function is updated for all the state and control spaces, instead of updating for a single state and a single control in traditional $ Q$ -learning algorithm. A new convergence criterion is established to guarantee that the iterative...
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