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Actor-Critic RL algorithms achieve optimal sample complexity for MDPs

By PulseAugur Editorial · [2 sources] ·

Two new arXiv papers explore advancements in actor-critic reinforcement learning algorithms. The first paper, though later withdrawn, proposed an optimal sample complexity of O(ε−2) for single-timescale actor-critic methods by using a sample buffer and momentum. The second paper introduces a novel optimistic actor-critic algorithm for low-rank MDPs that relies solely on policy evaluation, achieving improved sample complexity without computationally expensive oracles. AI

IMPACT These papers advance theoretical understanding of reinforcement learning, potentially leading to more efficient training of agents in complex environments.

RANK_REASON Two arXiv papers present theoretical advancements in reinforcement learning algorithms.

Read on arXiv cs.LG →

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

Actor-Critic RL algorithms achieve optimal sample complexity for MDPs

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Navdeep Kumar, Tehila Dahan, Lior Cohen, Ananyabrata Barua, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor ·

    Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum

    arXiv:2602.01505v2 Announce Type: replace Abstract: We establish an optimal sample complexity of $O(\epsilon^{-2})$ for obtaining an $\epsilon$-optimal global policy using a single-timescale actor-critic (AC) algorithm in infinite-horizon discounted Markov decision processes (MDP…

  2. arXiv cs.LG TIER_1 English(EN) · Ruiquan Huang, Donghao Li, Yingbin Liang, Jing Yang ·

    Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs

    arXiv:2605.01242v1 Announce Type: new Abstract: Reinforcement learning (RL) is a fundamental framework for sequential decision-making, in which an agent learns an optimal policy through interactions with an unknown environment. In settings with function approximation, many existi…

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