Pith Number

pith:QYSKLZTX

pith:2016:QYSKLZTX43ZBBRRR26DFGLFKWG

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OpenAI Gym

Greg Brockman, Jie Tang, John Schulman, Jonas Schneider, Ludwig Pettersson, Vicki Cheung, Wojciech Zaremba

A toolkit supplies benchmark problems for reinforcement learning through a shared interface along with a website for comparing algorithm results.

arxiv:1606.01540 v1 · 2016-06-05 · cs.LG · cs.AI

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ 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.

Claims

C1strongest claim

OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms.

C2weakest assumption

That a common interface plus public result sharing will be sufficient to drive meaningful progress and fair comparisons, without addressing whether the chosen environments adequately represent real-world complexity or diversity of tasks.

C3one line summary

OpenAI Gym introduces a common interface for reinforcement learning environments and a results-sharing website to enable consistent algorithm comparisons.

References

15 extracted · 15 resolved · 1 Pith anchors

[1] Dynamic programming and optimal control 1995

[2] V . Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, Sadik Beattie, C., Antonoglou A., H. I., King, D. Kum 2015

[3] J. Schulman, S. Levine, P. Abbeel, M. I. Jordan, and P. Moritz. Trust region policy optimization. In ICML, pages 1889–1897, 2015 2015

[4] Asynchronous Methods for Deep Reinforcement Learning 2016 · arXiv:1602.01783

[5] M. G. Bellemare, Y . Naddaf, J. Veness, and M. Bowling. The Arcade Learning Environment: An evaluation platform for general agents. J. Artif. Intell. Res., 47:253–279, 2013 2013

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Receipt and verification

First computed	2026-07-04T21:03:34.073509Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8624a5e677e6f210c631d786532caab184fd952f3f8ea1deaf845a89d7ed9ff7

Aliases

arxiv: 1606.01540 · arxiv_version: 1606.01540v1 · doi: 10.48550/arxiv.1606.01540 · pith_short_12: QYSKLZTX43ZB · pith_short_16: QYSKLZTX43ZBBRRR · pith_short_8: QYSKLZTX

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/QYSKLZTX43ZBBRRR26DFGLFKWG \
  | 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: 8624a5e677e6f210c631d786532caab184fd952f3f8ea1deaf845a89d7ed9ff7

Canonical record JSON

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    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2016-06-05T17:54:48Z",
    "title_canon_sha256": "56fea31d271806fa816a874361b3e508e0ba23bb9f3f10c323c9ecaa11c9b938"
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