Malliavin calculus reformulates counterfactual gradient estimation as ratios of unconditioned expectations to achieve standard convergence rates in adaptive IRL.
Sensitivity analysis using Ito–Malliavin calculus and martingales, and application to stochastic optimal control
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Malliavin Calculus for Counterfactual Gradient Estimation in Adaptive Inverse Reinforcement Learning
Malliavin calculus reformulates counterfactual gradient estimation as ratios of unconditioned expectations to achieve standard convergence rates in adaptive IRL.