FinanceIndustry#LLM#多模态#Neural Actor--Critic Learning#Minimax Critics#Reflected Langevin Sampling#Actor--critic

Neural Actor--Critic Learning with Minimax Critics and Reflected Langevin Sampling

arXiv AI/CL/LG（论文）T200:051d agoPaperRead the original ↗Export MD ↓

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进入全量动态池，后续可由人工或模型复核精选价值。

AI commentary

「来自 arXiv AI/CL/LG（论文）的这条信息指向行业格局和商业判断变化，适合用于判断近期 AI 圈的真实动向。」

Quick overview

Actor--critic methods rely on critic gradients to improve continuous-action policies, yet finite-sample guarantees must control both value and gradient errors and their propagation through the actor.
We develop a neural actor-critic procedure that combines a regularized minimax Bellman estimator over sigmoid networks with a finite reflected Langevin sampler driven by the fitted critic gradient.
Under uniform Bellman regularity and covered reset sampling, a positive high-order action-derivative penalty yields value and action-gradient risk bounds with classical nonparametric minimax exponents up to logarithmic …

Full text

Actor--critic methods rely on critic gradients to improve continuous-action policies, yet finite-sample guarantees must control both value and gradient errors and their propagation through the actor. We develop a neural actor-critic procedure that combines a regularized minimax Bellman estimator over sigmoid networks with a finite reflected Langevin sampler driven by the fitted critic gradient. Under uniform Bellman regularity and covered reset sampling, a positive high-order action-derivative penalty yields value and action-gradient risk bounds with classical nonparametric minimax exponents up to logarithmic factors. We propagate estimation, mixing, discretization, temperature, and policy-iteration errors to obtain return guarantees, with refinements for bounded multimodal potentials and strongly concave regimes. Empirical studies across controlled simulations, logged continuous-control data, and a simulated decision task show favorable performance relative to the considered baselines.

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