# Policy Gradient (REINFORCE)

In liquidation with linear market impact and a price driven by fractional Brownian motion, the deterministic Almgren-Chriss schedule is no longer optimal. A policy-gradient-trained neural network takes past price increments as input and maximizes CARA utility directly; the learned policy beats the Almgren-Chriss benchmark at every tested Hurst exponent, recovers it in the martingale case, improves more as the exponent moves away from 0.5, and gains more in the anti-persistent regime.

In liquidation with linear market impact and a price driven by fractional Brownian motion, the deterministic Almgren-Chriss schedule is no longer optimal. A policy-gradient-trained neural network takes past price increments as input and maximizes CARA utility directly; the learned policy beats the Almgren-Chriss benchmark at every tested Hurst exponent, recovers it in the martingale case, improves more as the exponent moves away from 0.5, and gains more in the anti-persistent regime.