  We present a method for finding optimal hedging policies for arbitrary
initial portfolios and market states. We develop a novel actor-critic algorithm
for solving general risk-averse stochastic control problems and use it to learn
hedging strategies across multiple risk aversion levels simultaneously. We
demonstrate the effectiveness of the approach with a numerical example in a
stochastic volatility environment.
