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New Q-learning method achieves n^{-1/4} Gaussian approximation bound

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method for approximating Gaussian distributions in entropy-regularized Q-learning with function approximation. The study establishes convergence rates for averaged iterates generated by asynchronous Q-learning, achieving a Gaussian approximation bound with a rate of order n^{-1/4}. This work combines linearization of the soft Bellman recursion with a Gaussian approximation for the leading martingale term, also deriving high-order moment bounds for the algorithm's final iterate. AI

IMPACT Establishes theoretical bounds for Q-learning algorithms, potentially improving sample efficiency in reinforcement learning applications.

RANK_REASON The cluster contains an academic paper detailing a new theoretical result in machine learning.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Q-learning method achieves n^{-1/4} Gaussian approximation bound

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Artemy Rubtsov, Rahul Singh, Eric Moulines, Alexey Naumov, Sergey Samsonov ·

    On Gaussian approximation for entropy-regularized Q-learning with function approximation

    arXiv:2605.17678v1 Announce Type: new Abstract: In this paper, we derive rates of convergence in the high-dimensional central limit theorem for Polyak--Ruppert averaged iterates generated by entropy-regularized asynchronous Q-learning with linear function approximation and a poly…

  2. arXiv stat.ML TIER_1 English(EN) · Sergey Samsonov ·

    On Gaussian approximation for entropy-regularized Q-learning with function approximation

    In this paper, we derive rates of convergence in the high-dimensional central limit theorem for Polyak--Ruppert averaged iterates generated by entropy-regularized asynchronous Q-learning with linear function approximation and a polynomial stepsize $k^{-ω}$, $ω\in (1/2,1)$. Assumi…

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