The paper claims entropy regularization forces the optimal LQ feedback policy to be Gaussian and uses that to solve a mean-variance asset-liability problem, but the proof of the main theorem contains a correlation error that invalidates the general claim.
Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning
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abstract
This paper studies a discrete-time mean-variance model based on reinforcement learning. Compared with its continuous-time counterpart in \cite{zhou2020mv}, the discrete-time model makes more general assumptions about the asset's return distribution. Using entropy to measure the cost of exploration, we derive the optimal investment strategy, whose density function is also Gaussian type. Additionally, we design the corresponding reinforcement learning algorithm. Both simulation experiments and empirical analysis indicate that our discrete-time model exhibits better applicability when analyzing real-world data than the continuous-time model.
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Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application
The paper claims entropy regularization forces the optimal LQ feedback policy to be Gaussian and uses that to solve a mean-variance asset-liability problem, but the proof of the main theorem contains a correlation error that invalidates the general claim.