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Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations.
Deep Nonlinear Stochastic Optimal Control for Systems with Multiplicative Uncertainties
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations.
MPC-Inspired Neural Network Policies for Sequential Decision Making
In this paper we investigate the use of MPC-inspired neural network policies for sequential decision making.
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