ENT-00002792 · Benchmark

ALE Space Invaders

ALE Space Invaders is a reinforcement learning environment associated with Arcade Learning Environment.

Level V — PersistentActiveDocumented

Human readable record

Overview

ALE Space Invaders is an Atari 2600 environment within the Arcade Learning Environment. It is used as a reinforcement learning benchmark task for evaluating agents under visual observation, reward feedback and sequential decision-making constraints.

Timeline

2013Public release or documentation
ALE Space Invaders appears in public documentation, project records, benchmark descriptions or research references associated with Arcade Learning Environment.

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 ALE Space Invaders. 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 LearningAtariAgent Evaluation

Relationship graph

ALE Space Invaders

Associated organizationArcade Learning Environment

Arcade Learning Environment is the organization, project community or institutional context associated with ALE Space Invaders.

Domain contextReinforcement learning evaluation

ALE Space Invaders 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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    "name": "ALE Space Invaders",
    "alternateName": [],
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    "creator": "Arcade Learning Environment contributors",
    "url": "entity.php?id=ENT-00002792",
    "sameAs": "https://ale.farama.org/",
    "lxkeysWorld": {
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        "organization": "Arcade Learning Environment",
        "originContext": "Reinforcement learning evaluation",
        "firstPublicAppearance": "2013",
        "currentStatus": "Active",
        "registryStatus": "Documented",
        "spatiumIndex": {
            "total": 82,
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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"
        ],
        "limitations": [
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            "A benchmark measures a defined task scope rather than complete intelligence."
        ],
        "tags": [
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            "Agent Evaluation"
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}

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