Pith Number
pith:YOGCNHQB
pith:2021:YOGCNHQBK3Y7JNH2WN4LLP2ACK
not attested
not anchored
not stored
refs pending
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance
arxiv:2111.09159 v3 · 2021-11-17 · cs.LG · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{YOGCNHQBK3Y7JNH2WN4LLP2ACK}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Cited by
Receipt and verification
| First computed | 2026-07-05T05:16:49.655388Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
c38c269e0156f1f4b4fab378b5bf4012b34c365d2686ffdc38bb3b81c4025ba9
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/YOGCNHQBK3Y7JNH2WN4LLP2ACK \
| 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())"
# expect: c38c269e0156f1f4b4fab378b5bf4012b34c365d2686ffdc38bb3b81c4025ba9
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "122ccf4661d5a773ff62987e60c4c9041808f338b7bb58d031103fcb10c6e7f6",
"cross_cats_sorted": [
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2021-11-17T14:48:52Z",
"title_canon_sha256": "43c4fa06d0dc4dd0406e3631aeda074872e381b29cb490be3350a5853c9b8260"
},
"schema_version": "1.0",
"source": {
"id": "2111.09159",
"kind": "arxiv",
"version": 3
}
}