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Tight Sample Complexity Bounds for Entropic Best Policy Identification

Amer Essakine, Claire Vernade

A new stopping rule closes the exponential gap and matches the lower bound for entropic best-policy identification.

arxiv:2605.13717 v1 · 2026-05-13 · cs.LG · stat.ML

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Claims

C1strongest claim

We show that this extra exponential factor can be traced to overly loose concentration control for exponential utilities. [...] we propose a new stopping rule that exploits further this tightness to obtain a sample complexity that matches the lower bound.

C2weakest assumption

The smoothness properties of the exponential utility suffice to derive sharper concentration bounds that are tight enough for the new stopping rule to match the lower bound.

C3one line summary

New concentration bounds and stopping rule close the exponential gap to match the lower bound for entropic best policy identification.

References

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[1] Proceedings of the 29th International Conference on Machine Learning (ICML-12) , series = 2012
[2] arXiv preprint arXiv:2506.00286 , year =
[3] Computational Economics , year =
[4] Journal of Intelligent 2017
[5] Beyond Average Return in Markov Decision Processes , url =
Receipt and verification
First computed 2026-05-18T02:44:16.691728Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

b99148bbba381d2255dfcca2ef80aaa9dcfff2706ba2e078b19abee30bf1db86

Aliases

arxiv: 2605.13717 · arxiv_version: 2605.13717v1 · doi: 10.48550/arxiv.2605.13717 · pith_short_12: XGIURO52HAOS · pith_short_16: XGIURO52HAOSEVO7 · pith_short_8: XGIURO52
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/XGIURO52HAOSEVO7ZSRO7AFKVH \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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    "submitted_at": "2026-05-13T16:02:26Z",
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