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Reinforcement learning theory achieves new sample complexity for actor-critic methods

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have established a new theoretical sample complexity guarantee for off-policy actor-critic methods in reinforcement learning. The paper proves the first $\tilde{\mathcal{O}}(\epsilon^{-2})$ sample complexity for finding an $\epsilon$-optimal policy under minimal assumptions, specifically requiring only an irreducible Markov chain. This achievement contrasts with prior work that necessitated nested-loop updates or stronger, algorithm-dependent policy assumptions. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Establishes a new theoretical benchmark for reinforcement learning algorithms, potentially improving sample efficiency in future applications.

RANK_REASON Academic paper detailing a theoretical advance in reinforcement learning algorithms.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Zaiwei Chen ·

    Achieving $ε^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions

    In this paper, we establish last-iterate convergence rates for off-policy actor--critic methods in reinforcement learning. In particular, under a single-loop, single-timescale implementation and a broad class of policy updates, including approximate policy iteration and natural p…

  2. arXiv stat.ML TIER_1 · Ishaq Hamza, Zaiwei Chen ·

    Achieving $\epsilon^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions

    arXiv:2605.13639v1 Announce Type: cross Abstract: In this paper, we establish last-iterate convergence rates for off-policy actor--critic methods in reinforcement learning. In particular, under a single-loop, single-timescale implementation and a broad class of policy updates, in…

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