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Research paper analyzes variance reduction in Temporal Difference learning

By PulseAugur Editorial · [2 sources] ·

A new research paper analyzes the variance in Temporal Difference (TD) learning, a method used in reinforcement learning. The study demonstrates that TD learning can reduce variance by aggregating more independent trajectories, showing its variance is asymptotically bounded by Monte Carlo estimators. The research also introduces Direct Advantage Estimation (DAE) as a regression-adjusted control variate that offers tighter variance bounds than TD in large-sample scenarios. AI

IMPACT Provides theoretical insights into variance reduction techniques for reinforcement learning algorithms.

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical analysis and numerical illustrations of machine learning algorithms.

Read on arXiv cs.LG →

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

Research paper analyzes variance reduction in Temporal Difference learning

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Hsiao-Ru Pan, Bernhard Sch\"olkopf ·

    On the Variance of Temporal Difference Learning and its Reduction Using Control Variates

    arXiv:2606.20357v1 Announce Type: new Abstract: We analyze the variance of temporal difference (TD) learning using the phased setting with tabular representation, and show that one of the mechanisms behind its ability to reduce variance is by effectively aggregating over a larger…

  2. arXiv cs.LG TIER_1 English(EN) · Bernhard Schölkopf ·

    On the Variance of Temporal Difference Learning and its Reduction Using Control Variates

    We analyze the variance of temporal difference (TD) learning using the phased setting with tabular representation, and show that one of the mechanisms behind its ability to reduce variance is by effectively aggregating over a larger number of independent trajectories. Based on th…

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