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Optimal Rates of Convergence for Entropy Regularization in Discounted Markov Decision Processes
We study the error introduced by entropy regularization in infinite-horizon discrete discounted Markov decision processes. We show that this error decreases exponentially in the inverse regularization strength, both in a weighted KL-divergence and in value with a problem-specific exponent. This is i
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