| Abstract: |
| In this talk, we present a control method for solving a high-dimensional stochastic Hamiltonian system with boundary conditions, which is essentially a Forward Backward Stochastic Differential Equation (FBSDE in short). Different from existing methods, we first formulate a stochastic optimal control problem whose extended Hamiltonian system is exactly the system to be solved. Then two different algorithms to calculate the stochastic optimal control via deep neural networks are designed respectively. Comparing with the Deep FBSDE method, our proposed algorithms demonstrate more stable performance. |
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