Drift Lipschitz budget K yields 1/K value approximation for diffusion policies, with matching lower bound, and finite-sample rates Õ(n^{-2/(m+6)}) (generic) or Õ(n^{-2/(m+4)}) (dissipative).
A natural policy gradient.Advances in neural information processing systems, 14
2 Pith papers cite this work. Polarity classification is still indexing.
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Differential privacy in policy optimization adds sample complexity costs that often appear as lower-order terms rather than dominating the bounds.
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Expressivity and Statistical Trade-offs in Diffusion Policy Learning
Drift Lipschitz budget K yields 1/K value approximation for diffusion policies, with matching lower bound, and finite-sample rates Õ(n^{-2/(m+6)}) (generic) or Õ(n^{-2/(m+4)}) (dissipative).
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On the Sample Complexity of Differentially Private Policy Optimization
Differential privacy in policy optimization adds sample complexity costs that often appear as lower-order terms rather than dominating the bounds.