Policies trained under stationary latent ambiguity, implemented by refreshing the latent parameter, preserve robustness to regime shifts better than policies trained under a fixed latent draw.
Since the drift is known, the policy does not learn about it from its observations and the optimal investment amount is constant over time
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Robust Control under Stationary Ambiguity
Policies trained under stationary latent ambiguity, implemented by refreshing the latent parameter, preserve robustness to regime shifts better than policies trained under a fixed latent draw.