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New theory sharpens statistical rates for asynchronous TD learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new theoretical framework for asynchronous Temporal Difference (TD) learning, a key algorithm in reinforcement learning. The study provides sharp statistical rates for the last iterate of standard tabular TD learning, offering guarantees on error bounds with a specific number of transitions. This work allows for non-reversible Markov chains, arbitrary initial state distributions, and bounded rewards, with a proof methodology involving anchored local Poisson equations and hitting-time compensation identities. AI

IMPACT Provides theoretical underpinnings for reinforcement learning algorithms, potentially improving their efficiency and applicability.

RANK_REASON Academic paper detailing theoretical advancements in machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New theory sharpens statistical rates for asynchronous TD learning

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Academic paper detailing theoretical advancements in machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yang Peng ·

    Sharp Statistical Rates for Asynchronous TD Learning with Markovian Data

    arXiv:2609.38880v1 Announce Type: new Abstract: We study the last iterate of standard tabular temporal-difference (TD) learning from a single trajectory of a finite Markov reward process. For discount factor $\gamma$, write $H=(1-\gamma)^{-1}$, and let $\mu_{\min}$ and $t_{\opera…

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