{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "vinyals-et-al-alphastar-2019", "title": "Vinyals and colleagues: AlphaStar league-trained StarCraft II agent", "summary": "Combined human-game imitation with a league of main and exploiter policies, reaching Grandmaster-level results in evaluated online StarCraft II games for all three races.", "boundary": "Evaluation used stated camera and action constraints; result is not dominance in every match or transfer to arbitrary real-world multi-agent tasks.", "area": "Multi-agent reinforcement learning", "topic": "Artificial intelligence", "year": 2019, "lane": "HUMAN", "result": "EMPIRICAL", "package_id": "vinyals-et-al-alphastar-2019", "target": { "record_id": "urn:uuid:9901754c-7c6d-4685-af48-d06e11699e7d", "version": 1, "content_hash": "sha256:3bf1e0ffc224ba44a142ca5c6d866ef5621f41a5474d428cba92adbe8d6ccb23" }, "humans": [ "Oriol Vinyals", "Igor Babuschkin", "Wojciech M. Czarnecki", "Michaël Mathieu", "Andrew Dudzik", "Junyoung Chung", "David H. Choi", "Richard Powell", "Timo Ewalds", "Petko Georgiev", "Junhyuk Oh", "Dan Horgan", "Manuel Kroiss", "Ivo Danihelka", "Aja Huang", "Laurent Sifre", "Trevor Cai", "John P. Agapiou", "Max Jaderberg", "Alexander S. Vezhnevets", "Rémi Leblond", "Tobias Pohlen", "Valentin Dalibard", "David Budden", "Yury Sulsky", "James Molloy", "Tom L. Paine", "Caglar Gulcehre", "Ziyu Wang", "Tobias Pfaff", "Yuhuai Wu", "Roman Ring", "Dani Yogatama", "Dario Wünsch", "Katrina McKinney", "Oliver Smith", "Tom Schaul", "Timothy Lillicrap", "Koray Kavukcuoglu", "Demis Hassabis", "Chris Apps", "David Silver" ], "authors_complete": true, "human_role_note": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.", "agents": [], "related": [], "subresults": [], "scientific_assessment": { "source_review": "SOURCE_CURATED", "scientific_review": "NOT_REVIEWED_BY_WRS", "formal_replay": "NOT_RUN", "provider_attestation": "NONE" }, "sources": [ { "id": "cur20261015-nature-alphastar", "title": "Grandmaster level in StarCraft II using multi-agent reinforcement learning", "url": "https://www.nature.com/articles/s41586-019-1724-z" }, { "id": "cur20261015-support-vinyals-et-al-alphastar-2019", "title": "Vinyals and colleagues: AlphaStar league-trained StarCraft II agent — official research record", "url": "https://deepmind.google/blog/alphastar-grandmaster-level-in-starcraft-ii-using-multi-agent-reinforcement-learning/" }, { "id": "cur20261015-alphastar-crossref", "title": "Publisher-deposited complete AlphaStar byline", "url": "https://api.crossref.org/works/10.1038/s41586-019-1724-z" } ], "points": "200000", "published_grade": { "id": "vinyals-et-al-alphastar-2019", "entry_id": "vinyals-et-al-alphastar-2019", "band": "A", "modifier": "+", "target": { "record_id": "urn:uuid:9901754c-7c6d-4685-af48-d06e11699e7d", "version": 1, "content_hash": "sha256:3bf1e0ffc224ba44a142ca5c6d866ef5621f41a5474d428cba92adbe8d6ccb23" }, "what_changed": "Combined human-game imitation with a league of main and exploiter policies, reaching Grandmaster-level results in evaluated online StarCraft II games for all three races.", "scope": "Combined human-game imitation with a league of main and exploiter policies, reaching Grandmaster-level results in evaluated online StarCraft II games for all three races. 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A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nOriol Vinyals; Igor Babuschkin; Wojciech M. Czarnecki; Michaël Mathieu; Andrew Dudzik; Junyoung Chung; David H. Choi; Richard Powell; Timo Ewalds; Petko Georgiev; Junhyuk Oh; Dan Horgan; Manuel Kroiss; Ivo Danihelka; Aja Huang; Laurent Sifre; Trevor Cai; John P. Agapiou; Max Jaderberg; Alexander S. Vezhnevets; Rémi Leblond; Tobias Pohlen; Valentin Dalibard; David Budden; Yury Sulsky; James Molloy; Tom L. Paine; Caglar Gulcehre; Ziyu Wang; Tobias Pfaff; Yuhuai Wu; Roman Ring; Dani Yogatama; Dario Wünsch; Katrina McKinney; Oliver Smith; Tom Schaul; Timothy Lillicrap; Koray Kavukcuoglu; Demis Hassabis; Chris Apps; David Silver\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Grandmaster level in StarCraft II using multi-agent reinforcement learning](https://www.nature.com/articles/s41586-019-1724-z)\n- [Vinyals and colleagues: AlphaStar league-trained StarCraft II agent — official research record](https://deepmind.google/blog/alphastar-grandmaster-level-in-starcraft-ii-using-multi-agent-reinforcement-learning/)\n- [Publisher-deposited complete AlphaStar byline](https://api.crossref.org/works/10.1038/s41586-019-1724-z)\n", "summary": "Combined human-game imitation with a league of main and exploiter policies, reaching Grandmaster-level results in evaluated online StarCraft II games for all three races.", "title": "Vinyals and colleagues: AlphaStar league-trained StarCraft II agent" }, "record_id": "urn:uuid:9901754c-7c6d-4685-af48-d06e11699e7d", "version": 1, "source_ref": { "record_id": "urn:uuid:9901754c-7c6d-4685-af48-d06e11699e7d", "version": 1, "content_hash": "sha256:3bf1e0ffc224ba44a142ca5c6d866ef5621f41a5474d428cba92adbe8d6ccb23" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:4d65292e56da6f285ffad3de51cce28ca02f892821e9758adc903bf4297d68ee" }, "target": { "record_id": "urn:uuid:9901754c-7c6d-4685-af48-d06e11699e7d", "version": 1, "content_hash": "sha256:3bf1e0ffc224ba44a142ca5c6d866ef5621f41a5474d428cba92adbe8d6ccb23" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "vinyals-et-al-alphastar-2019", "entry_id": "vinyals-et-al-alphastar-2019", "revision": 1, "title": "Vinyals and colleagues: AlphaStar league-trained StarCraft II agent", "context": "Multi-agent reinforcement learning", "track": "Human-designed research method or resource", "band": "A", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Combined human-game imitation with a league of main and exploiter policies, reaching Grandmaster-level results in evaluated online StarCraft II games for all three races.", "scope": "Combined human-game imitation with a league of main and exploiter policies, reaching Grandmaster-level results in evaluated online StarCraft II games for all three races. Evaluation used stated camera and action constraints; result is not dominance in every match or transfer to arbitrary real-world multi-agent tasks.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. 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