  This work focuses on the dynamic hedging of financial derivatives, where a
reinforcement learning algorithm is designed to minimize the variance of the
delta hedging process. In contrast to previous research in this area, we apply
uncertainty estimation technology to measure the uncertainty of the agent's
decision, which can further reduce unnecessary wear and tear in the hedging
process and control model overconfidence that may lead to significant losses.
Numerical experiments show the superiority of our strategy in Monte Carlo
simulations and SP 500 option data.
