A Single Stepsize Suffices for Unprojected Linear TD(0): Simultaneous Robust and Fast Rates via Polyak--Ruppert Averaging
arXiv:2606.24981v1 Announce Type: new 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 stepsize schedule $\eta_t \propto \frac{1}{\tau_{\mathrm{mix}}\log(t)\sqrt{t}}$ that depends on the mixing time but requires no prior knowledge of the curvature parameter $\omega$. Our first result shows that such a choice of the stepsize guarantees that the TD(0) iterates are automatically and uniformly bounded with hig
This is a theoretical machine learning paper addressing a specific algorithmic challenge (TD(0) convergence) which is an ongoing area of academic research.
For a sophisticated reader, this paper represents a incremental academic advance in reinforcement learning theory, potentially improving algorithmic robustness.
This research could lead to more robust and efficient reinforcement learning algorithms in the long term, reducing the need for extensive hyperparameter tuning for certain applications.
- · AI researchers
- · Reinforcement learning practitioners
Improved theoretical understanding of TD(0) algorithms for reinforcement learning.
Potentially more stable and easier-to-implement reinforcement learning systems in select use cases.
Slight acceleration of AI development if these theoretical improvements translate broadly into practical gains.
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