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).
Policy gradient methods for reinforcement learning with function approximation.Advances in neural information processing systems, 12, 1999
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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).