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New research challenges independence assumption in Deep Q-Learning algorithms

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new statistical analysis for Deep Q-Networks (DQN) that accounts for temporal dependence in training data. This approach models minibatches as $\tau$-mixing, moving beyond the typical assumption of independence. The findings indicate that temporal dependence can reduce the statistical rate of learning by introducing a dimensionality penalty, effectively lowering the sample size. AI

IMPACT Provides a more accurate theoretical understanding of deep reinforcement learning algorithms, potentially leading to more robust training methods.

RANK_REASON This is a research paper published on arXiv detailing a new theoretical framework and empirical validation for a machine learning algorithm.

Read on arXiv stat.ML →

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

New research challenges independence assumption in Deep Q-Learning algorithms

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Leon Halgryn (University of Twente), Sophie Langer (Ruhr-Universit\"at Bochum), Janusz M. Meylahn (University of Twente), E. Moritz Hahn (University of Twente) ·

    Beyond the Independence Assumption: Finite-Sample Guarantees for Deep Q-Learning under $\tau$-Mixing

    arXiv:2605.06373v1 Announce Type: cross Abstract: Finite-sample analyses of deep Q-learning typically treat replayed data as independent, even though it is sampled from temporally dependent state-action trajectories. We study the Deep Q-networks (DQN) algorithm under explicit dep…

  2. arXiv stat.ML TIER_1 English(EN) · E. Moritz Hahn ·

    Beyond the Independence Assumption: Finite-Sample Guarantees for Deep Q-Learning under $τ$-Mixing

    Finite-sample analyses of deep Q-learning typically treat replayed data as independent, even though it is sampled from temporally dependent state-action trajectories. We study the Deep Q-networks (DQN) algorithm under explicit dependence by modelling the minibatches used for upda…

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