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New TD(0) algorithm achieves robust and fast convergence with single stepsize

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method for linear TD(0) algorithms that uses a single stepsize schedule, eliminating the need for prior knowledge of curvature parameters. This approach provides high-probability guarantees for the algorithm's stability and convergence. The new stepsize schedule achieves both robust, curvature-free rates and fast, curvature-dependent rates simultaneously, offering a more efficient and stable solution for learning in Markovian environments. AI

IMPACT This research offers a more stable and efficient method for learning in Markovian environments, potentially improving reinforcement learning applications.

RANK_REASON The cluster contains an academic paper detailing a new algorithmic approach in machine learning.

Read on arXiv stat.ML →

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

New TD(0) algorithm achieves robust and fast convergence with single stepsize

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Wei-Cheng Lee, Francesco Orabona ·

    A Single Stepsize Suffices for Unprojected Linear TD(0): Simultaneous Robust and Fast Rates via Polyak--Ruppert Averaging

    arXiv:2606.24981v1 Announce Type: cross Abstract: We study linear TD(0) under Markovian sampling, where data are generated along a single trajectory. We provide high-probability guarantees for a plain unprojected TD(0) algorithm with Polyak-Ruppert (PR) averaging, using a single …

  2. arXiv stat.ML TIER_1 English(EN) · Francesco Orabona ·

    A Single Stepsize Suffices for Unprojected Linear TD(0): Simultaneous Robust and Fast Rates via Polyak--Ruppert Averaging

    We study linear TD(0) under Markovian sampling, where data are generated along a single trajectory. We provide high-probability guarantees for a plain unprojected TD(0) algorithm with Polyak-Ruppert (PR) averaging, using a single stepsize schedule $η_t \propto \frac{1}{τ_{\mathrm…

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