Entropy-regularized relaxed controls yield a truncated Gaussian optimal policy and a solvable nonlinear parabolic PDE for constrained portfolio optimization under stochastic volatility, enabling an implementable RL algorithm via martingale methods.
Simulation of square-root processes made simple: applications to the Heston model
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Optimal Investment and Entropy-Regularized Learning Under Stochastic Volatility Models with Portfolio Constraints
Entropy-regularized relaxed controls yield a truncated Gaussian optimal policy and a solvable nonlinear parabolic PDE for constrained portfolio optimization under stochastic volatility, enabling an implementable RL algorithm via martingale methods.