ENT-00002816 · Benchmark

DMC Cheetah Run

DMC Cheetah Run is a reinforcement learning environment associated with DeepMind Control Suite.

Level V — PersistentActiveDocumented

Human readable record

Overview

DMC Cheetah Run is a reinforcement learning environment documented as locomotion benchmark task. It provides a controlled task for evaluating policies, agents and sequential decision-making systems.

Timeline

2018Public release or documentation
DMC Cheetah Run appears in public documentation, project records, benchmark descriptions or research references associated with DeepMind Control Suite.

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 DMC Cheetah Run. 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 LearningAgent EvaluationEnvironment

Relationship graph

DMC Cheetah Run

Associated organizationDeepMind Control Suite

DeepMind Control Suite is the organization, project community or institutional context associated with DMC Cheetah Run.

Domain contextReinforcement learning evaluation

DMC Cheetah Run 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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    "creator": "DeepMind Control Suite",
    "url": "entity.php?id=ENT-00002816",
    "sameAs": "https://github.com/google-deepmind/dm_control",
    "lxkeysWorld": {
        "classification": "Reinforcement Learning Environment",
        "organization": "DeepMind Control Suite",
        "originContext": "Reinforcement learning evaluation",
        "firstPublicAppearance": "2018",
        "currentStatus": "Active",
        "registryStatus": "Documented",
        "spatiumIndex": {
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        "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",
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        },
        "capabilities": [
            "Evaluation protocol documentation",
            "Model comparison support",
            "Task taxonomy mapping",
            "Reproducible benchmark anchoring",
            "Relationship graph compatibility"
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        "limitations": [
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            "A benchmark measures a defined task scope rather than complete intelligence."
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}

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