On the stability analysis of deep neural network representations of an optimal state-feedback

6 Dec 2018Dario IzzoDharmesh TailorThomas Vasileiou

Recent work have shown how the optimal state-feedback, obtained as the solution to the Hamilton-Jacobi-Bellman equations, can be approximated for several nonlinear, deterministic systems by deep neural networks. When imitation (supervised) learning is used to train the neural network on optimal state-action pairs, for instance as derived by applying Pontryagin's theory of optimal processes, the resulting model is referred here as the guidance and control network... (read more)

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