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.
Large deviations for Markov processes and the asymptotic evaluation of certain Markov process expectations for large times, in: Probabilistic Methods in Differential Equations
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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.