{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MAOLEBAMV5CKJVICDHXUJCT6IZ","short_pith_number":"pith:MAOLEBAM","schema_version":"1.0","canonical_sha256":"601cb2040caf44a4d50219ef448a7e46468435b0efb57242ba5ae71f239e0b1e","source":{"kind":"arxiv","id":"2308.10284","version":1},"attestation_state":"computed","paper":{"title":"Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.MA"],"primary_cat":"cs.LG","authors_text":"Hadi Nekoei, Janarthanan Rajendran, Miao Liu, Sarath Chandar, Xutong Zhao","submitted_at":"2023-08-20T14:44:50Z","abstract_excerpt":"Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with Zero-Shot Coordination (ZSC) have gained significant attention in recent years. ZSC refers to the ability of agents to coordinate zero-shot (without additional interaction experience) with independently trained agents. While ZSC is crucial for cooperative MARL agents, it might not be possible for complex tasks and changing environments. Agents also need to adapt and improve their performance with minimal interaction with other agents. In this work, we show empirically that state-of-the-art ZSC algorithms have poor performanc"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2308.10284","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-20T14:44:50Z","cross_cats_sorted":["cs.AI","cs.MA"],"title_canon_sha256":"75948c21b1aebefa04c608daec15ed6cbae279943aee52c24050d749324274d6","abstract_canon_sha256":"ffdfc24c0c8c7b3a24b0878ffdb36d17c51292966b7dc70fac898e420d7580c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:43:07.203722Z","signature_b64":"QpE5XojdYz0wSuT05bkqAa8ZmJXYjUbC6Ut+3yJdo5EYJMiFe64RH4lbjh8MDps4HPBBMRH8cXwxNHorBVFMBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"601cb2040caf44a4d50219ef448a7e46468435b0efb57242ba5ae71f239e0b1e","last_reissued_at":"2026-07-05T06:43:07.203234Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:43:07.203234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.MA"],"primary_cat":"cs.LG","authors_text":"Hadi Nekoei, Janarthanan Rajendran, Miao Liu, Sarath Chandar, Xutong Zhao","submitted_at":"2023-08-20T14:44:50Z","abstract_excerpt":"Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with Zero-Shot Coordination (ZSC) have gained significant attention in recent years. ZSC refers to the ability of agents to coordinate zero-shot (without additional interaction experience) with independently trained agents. While ZSC is crucial for cooperative MARL agents, it might not be possible for complex tasks and changing environments. Agents also need to adapt and improve their performance with minimal interaction with other agents. In this work, we show empirically that state-of-the-art ZSC algorithms have poor performanc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.10284","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2308.10284/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2308.10284","created_at":"2026-07-05T06:43:07.203292+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.10284v1","created_at":"2026-07-05T06:43:07.203292+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.10284","created_at":"2026-07-05T06:43:07.203292+00:00"},{"alias_kind":"pith_short_12","alias_value":"MAOLEBAMV5CK","created_at":"2026-07-05T06:43:07.203292+00:00"},{"alias_kind":"pith_short_16","alias_value":"MAOLEBAMV5CKJVIC","created_at":"2026-07-05T06:43:07.203292+00:00"},{"alias_kind":"pith_short_8","alias_value":"MAOLEBAM","created_at":"2026-07-05T06:43:07.203292+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.03644","citing_title":"Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MAOLEBAMV5CKJVICDHXUJCT6IZ","json":"https://pith.science/pith/MAOLEBAMV5CKJVICDHXUJCT6IZ.json","graph_json":"https://pith.science/api/pith-number/MAOLEBAMV5CKJVICDHXUJCT6IZ/graph.json","events_json":"https://pith.science/api/pith-number/MAOLEBAMV5CKJVICDHXUJCT6IZ/events.json","paper":"https://pith.science/paper/MAOLEBAM"},"agent_actions":{"view_html":"https://pith.science/pith/MAOLEBAMV5CKJVICDHXUJCT6IZ","download_json":"https://pith.science/pith/MAOLEBAMV5CKJVICDHXUJCT6IZ.json","view_paper":"https://pith.science/paper/MAOLEBAM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.10284&json=true","fetch_graph":"https://pith.science/api/pith-number/MAOLEBAMV5CKJVICDHXUJCT6IZ/graph.json","fetch_events":"https://pith.science/api/pith-number/MAOLEBAMV5CKJVICDHXUJCT6IZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MAOLEBAMV5CKJVICDHXUJCT6IZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MAOLEBAMV5CKJVICDHXUJCT6IZ/action/storage_attestation","attest_author":"https://pith.science/pith/MAOLEBAMV5CKJVICDHXUJCT6IZ/action/author_attestation","sign_citation":"https://pith.science/pith/MAOLEBAMV5CKJVICDHXUJCT6IZ/action/citation_signature","submit_replication":"https://pith.science/pith/MAOLEBAMV5CKJVICDHXUJCT6IZ/action/replication_record"}},"created_at":"2026-07-05T06:43:07.203292+00:00","updated_at":"2026-07-05T06:43:07.203292+00:00"}