Provides non-asymptotic error bounds O(h^{1/4}) + O(M^{-γ}) for Euler discretization and interacting particle approximations of path-dependent MKV control, plus a neural policy-gradient method.
arXiv preprint arXiv:2309.04317 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A derivative-free projection algorithm with randomized neural networks solves high-dimensional stochastic optimal control and mean field control problems by regressing controls instead of minimizing loss via backpropagation.
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Numerical Approximation for Path-Dependent McKean-Vlasov Control with Non-Asymptotic Error Estimates
Provides non-asymptotic error bounds O(h^{1/4}) + O(M^{-γ}) for Euler discretization and interacting particle approximations of path-dependent MKV control, plus a neural policy-gradient method.
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An Effective Particle Gradient Projection Method for Solving Stochastic and Mean Field Control Problem
A derivative-free projection algorithm with randomized neural networks solves high-dimensional stochastic optimal control and mean field control problems by regressing controls instead of minimizing loss via backpropagation.