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This paper provides rigorous mathematical bounds on score functions and Lipschitz continuity for specific probability distributions used in reinforcement learning, such as Boltzmann and softmax policies. By formalizing these properties under explicit parameterizations, the work offers theoretical guarantees that are essential for analyzing the stability and convergence of advanced AI algorithms.

Source coveringarXiv

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Notes on Uniform Policy Score Bounds and Lipschitz Continuity for Truncated Gaussian, Product Cauchy, Boltzmann, and Softmax Policies

arXivOctober 14, 20261 min read

arXiv:2610.15690v1 Announce Type: cross Abstract: We establish uniform score bounds and Lipschitz continuity for truncated Gaussian, Cauchy, Boltzmann, and softmax policies under explicitly stated parameterizations.

Abstract:We establish uniform score bounds and Lipschitz continuity for truncated Gaussian, Cauchy, Boltzmann, and softmax policies under explicitly stated parameterizations.

Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR)

arXiv:2610.15690 [stat.ML]

(or arXiv:2610.15690v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.15690 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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