{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:FQFW662D4W4DE5JOOIGZ6TGVJN","short_pith_number":"pith:FQFW662D","canonical_record":{"source":{"id":"1905.05408","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-14T06:29:51Z","cross_cats_sorted":["cs.AI","cs.MA","stat.ML"],"title_canon_sha256":"fa1a0c1860de0606a37e2b29b4e10bc860ab9e1865a93de93f58b6d72f806582","abstract_canon_sha256":"091933b0d1639a2d72cd4dde94732d1ddb772687699b9f6b049882e2d4683ee9"},"schema_version":"1.0"},"canonical_sha256":"2c0b6f7b43e5b832752e720d9f4cd54b750b22d6eb46891cc54042f431a31c8b","source":{"kind":"arxiv","id":"1905.05408","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.05408","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"arxiv_version","alias_value":"1905.05408v1","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05408","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"pith_short_12","alias_value":"FQFW662D4W4D","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"FQFW662D4W4DE5JO","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"FQFW662D","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:FQFW662D4W4DE5JOOIGZ6TGVJN","target":"record","payload":{"canonical_record":{"source":{"id":"1905.05408","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-14T06:29:51Z","cross_cats_sorted":["cs.AI","cs.MA","stat.ML"],"title_canon_sha256":"fa1a0c1860de0606a37e2b29b4e10bc860ab9e1865a93de93f58b6d72f806582","abstract_canon_sha256":"091933b0d1639a2d72cd4dde94732d1ddb772687699b9f6b049882e2d4683ee9"},"schema_version":"1.0"},"canonical_sha256":"2c0b6f7b43e5b832752e720d9f4cd54b750b22d6eb46891cc54042f431a31c8b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:16.578631Z","signature_b64":"TlUWBgsr3fX/xLHpjK+M3IFFlpK2e3v0SL1zaJQzmZuKEoMX2l8pUl/BI/kEwgK9uvzR56egxUsB1Vw31rtwAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c0b6f7b43e5b832752e720d9f4cd54b750b22d6eb46891cc54042f431a31c8b","last_reissued_at":"2026-05-17T23:46:16.578020Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:16.578020Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.05408","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9CTLArwtDQQHaMPgm7M48KRCvY4bpI/j64LDW0prOP7xIShBS18QlyRtlFOn8M2MgeDgrsd+4WbAQiTZGh0KBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T17:56:11.462870Z"},"content_sha256":"c00f137ae359bb438c39844df15301b97f53fa6831584ebd07c5e9c37defaaaa","schema_version":"1.0","event_id":"sha256:c00f137ae359bb438c39844df15301b97f53fa6831584ebd07c5e9c37defaaaa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:FQFW662D4W4DE5JOOIGZ6TGVJN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MA","stat.ML"],"primary_cat":"cs.LG","authors_text":"Daewoo Kim, David Earl Hostallero, Kyunghwan Son, Wan Ju Kang, Yung Yi","submitted_at":"2019-05-14T06:29:51Z","abstract_excerpt":"We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently. However, VDN and QMIX are representative examples that use the idea of factorization of the joint action-value function into individual ones for decentralized execution. VDN and QMIX address only a fraction of factorizable MARL tasks due to their structural constraint in factorization such as additivity and monotonicity. In this paper, we propose a new factorization method for MARL, QTRAN, which is free from such struc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05408","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tXb9qA3IDsq7JlqqDGzLP9yZ+HtrocAXmzBVT9Qc0rvdJJE3AKwGtky1GUcv+WJOG9L9hB9y4m3JwZb8zethDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T17:56:11.463484Z"},"content_sha256":"5727819e22d02410de2788a1cbb22376f64865899e3670d343e6e906379f8cdb","schema_version":"1.0","event_id":"sha256:5727819e22d02410de2788a1cbb22376f64865899e3670d343e6e906379f8cdb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FQFW662D4W4DE5JOOIGZ6TGVJN/bundle.json","state_url":"https://pith.science/pith/FQFW662D4W4DE5JOOIGZ6TGVJN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FQFW662D4W4DE5JOOIGZ6TGVJN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T17:56:11Z","links":{"resolver":"https://pith.science/pith/FQFW662D4W4DE5JOOIGZ6TGVJN","bundle":"https://pith.science/pith/FQFW662D4W4DE5JOOIGZ6TGVJN/bundle.json","state":"https://pith.science/pith/FQFW662D4W4DE5JOOIGZ6TGVJN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FQFW662D4W4DE5JOOIGZ6TGVJN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:FQFW662D4W4DE5JOOIGZ6TGVJN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"091933b0d1639a2d72cd4dde94732d1ddb772687699b9f6b049882e2d4683ee9","cross_cats_sorted":["cs.AI","cs.MA","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-14T06:29:51Z","title_canon_sha256":"fa1a0c1860de0606a37e2b29b4e10bc860ab9e1865a93de93f58b6d72f806582"},"schema_version":"1.0","source":{"id":"1905.05408","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.05408","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"arxiv_version","alias_value":"1905.05408v1","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05408","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"pith_short_12","alias_value":"FQFW662D4W4D","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"FQFW662D4W4DE5JO","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"FQFW662D","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:5727819e22d02410de2788a1cbb22376f64865899e3670d343e6e906379f8cdb","target":"graph","created_at":"2026-05-17T23:46:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently. However, VDN and QMIX are representative examples that use the idea of factorization of the joint action-value function into individual ones for decentralized execution. VDN and QMIX address only a fraction of factorizable MARL tasks due to their structural constraint in factorization such as additivity and monotonicity. In this paper, we propose a new factorization method for MARL, QTRAN, which is free from such struc","authors_text":"Daewoo Kim, David Earl Hostallero, Kyunghwan Son, Wan Ju Kang, Yung Yi","cross_cats":["cs.AI","cs.MA","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-14T06:29:51Z","title":"QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05408","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c00f137ae359bb438c39844df15301b97f53fa6831584ebd07c5e9c37defaaaa","target":"record","created_at":"2026-05-17T23:46:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"091933b0d1639a2d72cd4dde94732d1ddb772687699b9f6b049882e2d4683ee9","cross_cats_sorted":["cs.AI","cs.MA","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-14T06:29:51Z","title_canon_sha256":"fa1a0c1860de0606a37e2b29b4e10bc860ab9e1865a93de93f58b6d72f806582"},"schema_version":"1.0","source":{"id":"1905.05408","kind":"arxiv","version":1}},"canonical_sha256":"2c0b6f7b43e5b832752e720d9f4cd54b750b22d6eb46891cc54042f431a31c8b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2c0b6f7b43e5b832752e720d9f4cd54b750b22d6eb46891cc54042f431a31c8b","first_computed_at":"2026-05-17T23:46:16.578020Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:16.578020Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TlUWBgsr3fX/xLHpjK+M3IFFlpK2e3v0SL1zaJQzmZuKEoMX2l8pUl/BI/kEwgK9uvzR56egxUsB1Vw31rtwAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:16.578631Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.05408","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c00f137ae359bb438c39844df15301b97f53fa6831584ebd07c5e9c37defaaaa","sha256:5727819e22d02410de2788a1cbb22376f64865899e3670d343e6e906379f8cdb"],"state_sha256":"6eda615befbe3f2baa619f1574784adfbb619a76f29f00cbdc04da56acd2ee11"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r9eafDxbajniqG2mTz3qXBeM7jSnTvGZ8yFQ4jXzw0kfcVQS/MsTLST//BxreaSEYsBJ7SFvGhXGXrb0R3JfCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T17:56:11.467166Z","bundle_sha256":"212ef0cf5de543e95da8f31ebaf346473f25b739a348593dd14616de48f18355"}}