An off-model training architecture using explicit dominating laws and Radon-Nikodym weights enables adaptive learning for non-Markovian stochastic control, with non-asymptotic error bounds separating Monte Carlo and model-risk errors.
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Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version
An off-model training architecture using explicit dominating laws and Radon-Nikodym weights enables adaptive learning for non-Markovian stochastic control, with non-asymptotic error bounds separating Monte Carlo and model-risk errors.