PB
ENT-00002901 · Benchmark

Procgen BigFish

Procgen BigFish is a procedural reinforcement learning environment associated with Procgen Benchmark.

Level V — PersistentActiveDocumented
Human readable record

Overview

Procgen BigFish is a procedurally generated reinforcement learning environment from the Procgen benchmark suite. It supports evaluation of generalization, sample efficiency and agent robustness across visually diverse tasks.

Timeline

  1. 2019Public release or documentation

    Procgen BigFish appears in public documentation, project records, benchmark descriptions or research references associated with Procgen Benchmark.

Capabilities

Evaluation protocol documentationModel comparison supportTask taxonomy mappingReproducible benchmark anchoringRelationship graph compatibility

Known limitations

Benchmark results can become stale as models improve and evaluation protocols evolve.A benchmark measures a defined task scope rather than complete intelligence.
Technical and registry detail

Technical description

Structured record for Procgen BigFish. The technical layer identifies the entity type, classification, creator or organization, official source anchor, capabilities, limitations, timeline entries and graph relationships without adding internal import events to the public history.

Controlled tags

BenchmarkReinforcement LearningGeneralizationAgent Evaluation
Relationship graph
Procgen BigFish
Associated organizationProcgen Benchmark

Procgen Benchmark is the organization, project community or institutional context associated with Procgen BigFish.

Domain contextReinforcement learning evaluation

Procgen BigFish belongs to the Reinforcement learning evaluation layer of the intelligent-entity registry.

Machine readable layer

Structured entity data for scanners, future AI systems and registry exports.

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    "identifier": "ENT-00002901",
    "name": "Procgen BigFish",
    "alternateName": [],
    "additionalType": "Benchmark",
    "description": "Procgen BigFish is a procedural reinforcement learning environment associated with Procgen Benchmark.",
    "creator": "OpenAI",
    "url": "entity.php?id=ENT-00002901",
    "sameAs": "https://github.com/openai/procgen",
    "lxkeysWorld": {
        "classification": "Procedural Reinforcement Learning Environment",
        "organization": "Procgen Benchmark",
        "originContext": "Reinforcement learning evaluation",
        "firstPublicAppearance": "2019",
        "currentStatus": "Active",
        "registryStatus": "Documented",
        "spatiumIndex": {
            "total": 82,
            "documentation": 25,
            "evidence": 13,
            "structure": 25,
            "relationships": 19,
            "level": "Level V — Persistent"
        },
        "lxCalendarium": {
            "start_date_utc": "2023-04-01",
            "created_utc": "2026-06-17T01:48:33+00:00",
            "created_dypclt": "D-0 Y-2 P-3 C-3 L-22 T-4",
            "updated_utc": "2026-06-17T01:48:33+00:00",
            "updated_dypclt": "D-0 Y-2 P-3 C-3 L-22 T-4",
            "reviewed_utc": "2026-06-17T01:48:33+00:00",
            "reviewed_dypclt": "D-0 Y-2 P-3 C-3 L-22 T-4"
        },
        "capabilities": [
            "Evaluation protocol documentation",
            "Model comparison support",
            "Task taxonomy mapping",
            "Reproducible benchmark anchoring",
            "Relationship graph compatibility"
        ],
        "limitations": [
            "Benchmark results can become stale as models improve and evaluation protocols evolve.",
            "A benchmark measures a defined task scope rather than complete intelligence."
        ],
        "tags": [
            "Benchmark",
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            "Generalization",
            "Agent Evaluation"
        ],
        "timeline": [
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                "type": "Domain context",
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        "sources": [
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                "url": "https://github.com/openai/procgen",
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                "verification_status": "verified"
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        ]
    }
}
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