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New framework learns transferable policies from action-free time series data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for learning control policies from action-free time series data. This hierarchical model-based reinforcement learning approach uses shared structure across related systems to reconstruct dynamics and parameterize policies. The method demonstrated improved transfer learning over independently trained policies on Lorenz-63 and double-pendulum systems. It also showed comparable performance to methods trained with controlled interactions and generalized to new systems after embedding inference alone. AI

IMPACT This research could enable more efficient training of control policies in robotics and other domains by reducing the need for direct human intervention during data collection.

RANK_REASON Academic paper detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework learns transferable policies from action-free time series data

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Academic paper detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Niklas Emonds, Georgia Koppe ·

    Learning Transferable Policies from Action-free Time Series Through Dynamical Embeddings

    arXiv:2610.03065v1 Announce Type: new Abstract: Learning control from action-free recordings is challenging because intervention effects are unobserved and policies may exploit errors in reconstructed dynamics. We present a hierarchical model-based reinforcement learning framewor…

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