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arXiv · 2105.13708

Convergence of the Value Function in Optimal Control Problems with Unknown Dynamics

Andrea Pesare·Michele Palladino·Maurizio Falcone
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Abstract

We deal with the convergence of the value function of an approximate control problem with uncertain dynamics to the value function of a nonlinear optimal control problem. The assumptions on the dynamics and the costs are rather general and we assume to represent uncertainty in the dynamics by a probability distribution. The proposed framework aims to describe and motivate some model-based Reinforcement Learning algorithms where the model is probabilistic. We also show some numerical experiments which confirm the theoretical results.

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Andrea Pesare, Michele Palladino, Maurizio Falcone. 2021-05-28. Convergence of the Value Function in Optimal Control Problems with Unknown Dynamics. https://arxiv.org/abs/2105.13708

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