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arXiv · 2509.08933

Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates

Sreejeet Maity·Aritra Mitra
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Abstract

We study the problem of learning the optimal policy in a discounted, infinite-horizon reinforcement learning (RL) setting in the presence of adversarially corrupted rewards. To address this problem, we develop a novel robust variant of the \(Q\)-learning algorithm and analyze it under the challenging asynchronous sampling model with time-correlated data. Despite corruption, we prove that the finite-time guarantees of our approach match existing bounds, up to an additive term that scales with the fraction of corrupted samples. We also establish an information-theoretic lower bound, revealing that our guarantees are near-optimal. Notably, our algorithm is agnostic to the underlying reward distribution and provides the first finite-time robustness guarantees for asynchronous \(Q\)-learning. A key element of our analysis is a refined Azuma-Hoeffding inequality for almost-martingales, which may have broader applicability in the study of RL algorithms.

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Sreejeet Maity, Aritra Mitra. 2025-09-10. Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates. https://arxiv.org/abs/2509.08933

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