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

Abstract: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.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2611.03352 [stat.ML]

(or arXiv:2611.03352v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2611.03352 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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