Optimal Investment and Entropy-Regularized Learning Under Stochastic Volatility Models with Portfolio Constraints
The study optimizes portfolio selection in continuous time under stochastic volatility using entropy regularization. It derives an associated Hamilton-Jacobi-Bellman equation, showing that the optimal exploratory policy is a truncated Gaussian distribution. This provides a mathematical framework for continuous-time reinforcement learning with constraints.