{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:LAGJIX6IHST65KJTMLLGV5HS65","short_pith_number":"pith:LAGJIX6I","schema_version":"1.0","canonical_sha256":"580c945fc83ca7eea93362d66af4f2f751248a64e700643d59a209b115874c5a","source":{"kind":"arxiv","id":"2112.06771","version":2},"attestation_state":"computed","paper":{"title":"Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.MA"],"primary_cat":"cs.AI","authors_text":"Bin Zhang, Chen Gong, Guoliang Fan, Xinwen Hou, Yu Liu, Yunpeng Bai","submitted_at":"2021-12-09T08:40:38Z","abstract_excerpt":"Recent years have witnessed the great success of multi-agent systems (MAS). Value decomposition, which decomposes joint action values into individual action values, has been an important work in MAS. However, many value decomposition methods ignore the coordination among different agents, leading to the notorious \"lazy agents\" problem. To enhance the coordination in MAS, this paper proposes HyperGraph CoNvolution MIX (HGCN-MIX), a method that incorporates hypergraph convolution with value decomposition. HGCN-MIX models agents as well as their relationships as a hypergraph, where agents are nod"},"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":"2112.06771","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2021-12-09T08:40:38Z","cross_cats_sorted":["cs.LG","cs.MA"],"title_canon_sha256":"cfd064122b9d783b5e24fbf6ff66a08c4ee2a2eb5d67cd15d7c18fa4a58ef3dc","abstract_canon_sha256":"2027ed910b7c5440141d248074da099156bbb34bb656f39fc519d0652133f2da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:18:41.244698Z","signature_b64":"fPErgoFLaO0+63dWpuHCM96LhEHf+pAlJ0fnCfngeHp2nMReeXiMrb0fy1Y77pz1QVfy4drS1FWVHIqYwEe/DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"580c945fc83ca7eea93362d66af4f2f751248a64e700643d59a209b115874c5a","last_reissued_at":"2026-07-05T04:18:41.244189Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:18:41.244189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.MA"],"primary_cat":"cs.AI","authors_text":"Bin Zhang, Chen Gong, Guoliang Fan, Xinwen Hou, Yu Liu, Yunpeng Bai","submitted_at":"2021-12-09T08:40:38Z","abstract_excerpt":"Recent years have witnessed the great success of multi-agent systems (MAS). Value decomposition, which decomposes joint action values into individual action values, has been an important work in MAS. However, many value decomposition methods ignore the coordination among different agents, leading to the notorious \"lazy agents\" problem. To enhance the coordination in MAS, this paper proposes HyperGraph CoNvolution MIX (HGCN-MIX), a method that incorporates hypergraph convolution with value decomposition. HGCN-MIX models agents as well as their relationships as a hypergraph, where agents are nod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.06771","kind":"arxiv","version":2},"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/2112.06771/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":"2112.06771","created_at":"2026-07-05T04:18:41.244255+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.06771v2","created_at":"2026-07-05T04:18:41.244255+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.06771","created_at":"2026-07-05T04:18:41.244255+00:00"},{"alias_kind":"pith_short_12","alias_value":"LAGJIX6IHST6","created_at":"2026-07-05T04:18:41.244255+00:00"},{"alias_kind":"pith_short_16","alias_value":"LAGJIX6IHST65KJT","created_at":"2026-07-05T04:18:41.244255+00:00"},{"alias_kind":"pith_short_8","alias_value":"LAGJIX6I","created_at":"2026-07-05T04:18:41.244255+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.10142","citing_title":"Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review","ref_index":74,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LAGJIX6IHST65KJTMLLGV5HS65","json":"https://pith.science/pith/LAGJIX6IHST65KJTMLLGV5HS65.json","graph_json":"https://pith.science/api/pith-number/LAGJIX6IHST65KJTMLLGV5HS65/graph.json","events_json":"https://pith.science/api/pith-number/LAGJIX6IHST65KJTMLLGV5HS65/events.json","paper":"https://pith.science/paper/LAGJIX6I"},"agent_actions":{"view_html":"https://pith.science/pith/LAGJIX6IHST65KJTMLLGV5HS65","download_json":"https://pith.science/pith/LAGJIX6IHST65KJTMLLGV5HS65.json","view_paper":"https://pith.science/paper/LAGJIX6I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.06771&json=true","fetch_graph":"https://pith.science/api/pith-number/LAGJIX6IHST65KJTMLLGV5HS65/graph.json","fetch_events":"https://pith.science/api/pith-number/LAGJIX6IHST65KJTMLLGV5HS65/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LAGJIX6IHST65KJTMLLGV5HS65/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LAGJIX6IHST65KJTMLLGV5HS65/action/storage_attestation","attest_author":"https://pith.science/pith/LAGJIX6IHST65KJTMLLGV5HS65/action/author_attestation","sign_citation":"https://pith.science/pith/LAGJIX6IHST65KJTMLLGV5HS65/action/citation_signature","submit_replication":"https://pith.science/pith/LAGJIX6IHST65KJTMLLGV5HS65/action/replication_record"}},"created_at":"2026-07-05T04:18:41.244255+00:00","updated_at":"2026-07-05T04:18:41.244255+00:00"}