A neural-network actor-critic policy gradient algorithm with squashed Gaussian C-vine policies solves high-dimensional robust pricing problems in the uncertain volatility model and outperforms existing Monte Carlo and ML benchmarks in numerical tests.
Policy evaluation and temporal-difference learning in continuous time and space: A martingale approach
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Stochastic Policy Gradient Methods in the Uncertain Volatility Model
A neural-network actor-critic policy gradient algorithm with squashed Gaussian C-vine policies solves high-dimensional robust pricing problems in the uncertain volatility model and outperforms existing Monte Carlo and ML benchmarks in numerical tests.