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We introduce a new closed-loop architecture for the online solution ofapproximate optimal control problems in the context of continuous-time systems.Specifically, we introduce the first algorithm that incorporates dynamicmomentum in actor-critic structures to control continuous-time dynamic plantswith an affine structure in the input. By incorporating dynamic momentum in ouralgorithm, we are able to accelerate the convergence properties of theclosed-loop system, achieving superior transient performance compared totraditional gradient-descent based techniques. In addition, by leveraging theexistence of past recorded data with sufficiently rich information properties,we dispense with the persistence of excitation condition traditionally imposedon the regressors of the critic and the actor. Given that our continuous-timemomentum-based dynamics also incorporate periodic discrete-time resets thatemulate restarting techniques used in the machine learning literature, weleverage tools from hybrid dynamical systems theory to establish asymptoticstability properties for the closed-loop system. We illustrate our results witha numerical example.

Accelerated Continuous-Time Approximate Dynamic Programming via Data-Assisted Hybrid Control