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[~Re] Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress

A replication of exploration in model-based reinforcement learning using estimates of learning progress.

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External publication. Published by its original venue; not published in our journal.

A QUESTION TO TAKE FURTHER

Does an exploration advantage survive across fixed environment seeds when interaction budgets are equal?

The evaluator owns environment seeds, interaction counters and return computation, and records unsuccessful or incomplete runs.

What you could produce

  • Versioned protocol, input and environment manifest, and independent per-case comparison table including uncertainty and incomplete cases.

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  • No research code was executed; no independent scientific verification has been performed.
  • The current qualified pilot is self-contained Python 3.13 with a 90-second author deadline. This article's environment has not been qualified for that path.
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Source and permission context

Chartouny, Augustin; Nadal, Jean-Pierre; Khamassi, Mehdi. [~Re] Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress. ReScience C 9(1), #6; 10.5281/zenodo.13627804.

Catalog listing reviewed. This review covers the description and source links displayed here.

ReScience/Zenodo metadata reviewed; exact CC-BY-4.0 evidence is manuscript-scoped.

Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.

manuscript · CC-BY-4.0

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