cvr-optimization

This paper extends an Extreme Value Theory approach for CVaR optimization in reinforcement learning to high-dimensional neural-network policies, obtaining a differentiable tail-risk objective computable via automatic differentiation, and shows improved hedging performance under extreme scenarios compared to empirical-CVaR-trained policies.