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In entropy-regularized reinforcement learning, the optimal policy is exactly of the Gibbs distribution form, and the coefficient of the entropy regularization term and the temperature are reciprocals of each other, making maximizing the entropy-regularized reward mathematically equivalent to matching the Gibbs distribution.
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10/16/2026
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