Particle methods with deep learning approximate partially observed stochastic control problems, with a convergence proof and tests on linear-quadratic, nonlinear mean-field, and financial examples.
arXiv preprint arXiv:2306.04788 , year=
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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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Particle Methods with Deep Learning for Stochastic Control under Partial Observation
Particle methods with deep learning approximate partially observed stochastic control problems, with a convergence proof and tests on linear-quadratic, nonlinear mean-field, and financial examples.
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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.