Recursive Regression with Neural Networks: Approximating the HJI PDE Solution

8 Nov 2016 Vicenç Rubies Royo Claire Tomlin

The majority of methods used to compute approximations to the Hamilton-Jacobi-Isaacs partial differential equation (HJI PDE) rely on the discretization of the state space to perform dynamic programming updates. This type of approach is known to suffer from the curse of dimensionality due to the exponential growth in grid points with the state dimension... (read more)

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