{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:46A7WS5XGJE7Q65ACISU4IQLZL","short_pith_number":"pith:46A7WS5X","canonical_record":{"source":{"id":"2212.11498","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-22T06:18:41Z","cross_cats_sorted":["cs.AI","cs.MA","cs.RO"],"title_canon_sha256":"c9545e781164ecacd1e81e9757538b44fb25a72f67a9dc8312acdbcdeff0a2bc","abstract_canon_sha256":"6cc198af4652d3918f7a04ce19bcfc22d698c4911e8773cfd5ab040f9d479877"},"schema_version":"1.0"},"canonical_sha256":"e781fb4bb73249f87ba012254e220bcaf411711d1ad80e6d489355d1b82cdd6b","source":{"kind":"arxiv","id":"2212.11498","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.11498","created_at":"2026-07-05T09:00:54Z"},{"alias_kind":"arxiv_version","alias_value":"2212.11498v3","created_at":"2026-07-05T09:00:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.11498","created_at":"2026-07-05T09:00:54Z"},{"alias_kind":"pith_short_12","alias_value":"46A7WS5XGJE7","created_at":"2026-07-05T09:00:54Z"},{"alias_kind":"pith_short_16","alias_value":"46A7WS5XGJE7Q65A","created_at":"2026-07-05T09:00:54Z"},{"alias_kind":"pith_short_8","alias_value":"46A7WS5X","created_at":"2026-07-05T09:00:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:46A7WS5XGJE7Q65ACISU4IQLZL","target":"record","payload":{"canonical_record":{"source":{"id":"2212.11498","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-22T06:18:41Z","cross_cats_sorted":["cs.AI","cs.MA","cs.RO"],"title_canon_sha256":"c9545e781164ecacd1e81e9757538b44fb25a72f67a9dc8312acdbcdeff0a2bc","abstract_canon_sha256":"6cc198af4652d3918f7a04ce19bcfc22d698c4911e8773cfd5ab040f9d479877"},"schema_version":"1.0"},"canonical_sha256":"e781fb4bb73249f87ba012254e220bcaf411711d1ad80e6d489355d1b82cdd6b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:00:54.545067Z","signature_b64":"4Rgo0R8oIQBvWNX3SfdUuQAocQyA79gRTwe+2l7o3MSgo8mv7L5nzCqYEezMuDIg1RYm0ND+esv0CTwHBm0BBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e781fb4bb73249f87ba012254e220bcaf411711d1ad80e6d489355d1b82cdd6b","last_reissued_at":"2026-07-05T09:00:54.544605Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:00:54.544605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.11498","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:00:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oHfYLjP6bQsBpyI/yDMTnG7SWUMsSvZ/q77CuT6L0MvzKoPB96u6IYTNrYgZUNCcnjzBu88DdZg35IkRxV00CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T19:01:51.205559Z"},"content_sha256":"9b74dbd5a759562ab128255092b37e2b5e00dad928520b861729683ee63a55df","schema_version":"1.0","event_id":"sha256:9b74dbd5a759562ab128255092b37e2b5e00dad928520b861729683ee63a55df"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:46A7WS5XGJE7Q65ACISU4IQLZL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MA","cs.RO"],"primary_cat":"cs.LG","authors_text":"Aleksandar Krnjaic, Andrew Wing Keung To, Georgios Papoudakis, Jonathan D. Thomas, Kuan-Ho Lao, Lukas Sch\\\"afer, Matthew Haley, Murat Cubuktepe, Peter B\\\"orsting, Raul D. Steleac, Stefano V. Albrecht","submitted_at":"2022-12-22T06:18:41Z","abstract_excerpt":"We consider a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, called the order-picking problem, is how these worker agents must coordinate their movement and actions in the warehouse to maximise performance in this task. Established industry methods using heuristic approaches require large engineering efforts to optimise for innately variable warehouse configurations. In contrast, multi-agent reinforcement learning (MARL) can be flexibly applied to diverse warehouse configurations ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.11498","kind":"arxiv","version":3},"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/2212.11498/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:00:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jdG6oJVOeptnuxxL9P2rXSwa4I246BejJ2CgHD2VHN8+uqkBtW8sEdQoEe+pcTN+hzc39KJM5Zb2oSG8YS4UAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T19:01:51.206076Z"},"content_sha256":"f38879c3271e67b1ca4e2c316e2c3d2a85e2fea7eb74100d5a1a0ab230a83bed","schema_version":"1.0","event_id":"sha256:f38879c3271e67b1ca4e2c316e2c3d2a85e2fea7eb74100d5a1a0ab230a83bed"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/46A7WS5XGJE7Q65ACISU4IQLZL/bundle.json","state_url":"https://pith.science/pith/46A7WS5XGJE7Q65ACISU4IQLZL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/46A7WS5XGJE7Q65ACISU4IQLZL/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":"2026-08-11T19:01:51Z","links":{"resolver":"https://pith.science/pith/46A7WS5XGJE7Q65ACISU4IQLZL","bundle":"https://pith.science/pith/46A7WS5XGJE7Q65ACISU4IQLZL/bundle.json","state":"https://pith.science/pith/46A7WS5XGJE7Q65ACISU4IQLZL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/46A7WS5XGJE7Q65ACISU4IQLZL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:46A7WS5XGJE7Q65ACISU4IQLZL","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":"6cc198af4652d3918f7a04ce19bcfc22d698c4911e8773cfd5ab040f9d479877","cross_cats_sorted":["cs.AI","cs.MA","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-22T06:18:41Z","title_canon_sha256":"c9545e781164ecacd1e81e9757538b44fb25a72f67a9dc8312acdbcdeff0a2bc"},"schema_version":"1.0","source":{"id":"2212.11498","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.11498","created_at":"2026-07-05T09:00:54Z"},{"alias_kind":"arxiv_version","alias_value":"2212.11498v3","created_at":"2026-07-05T09:00:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.11498","created_at":"2026-07-05T09:00:54Z"},{"alias_kind":"pith_short_12","alias_value":"46A7WS5XGJE7","created_at":"2026-07-05T09:00:54Z"},{"alias_kind":"pith_short_16","alias_value":"46A7WS5XGJE7Q65A","created_at":"2026-07-05T09:00:54Z"},{"alias_kind":"pith_short_8","alias_value":"46A7WS5X","created_at":"2026-07-05T09:00:54Z"}],"graph_snapshots":[{"event_id":"sha256:f38879c3271e67b1ca4e2c316e2c3d2a85e2fea7eb74100d5a1a0ab230a83bed","target":"graph","created_at":"2026-07-05T09:00:54Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2212.11498/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We consider a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, called the order-picking problem, is how these worker agents must coordinate their movement and actions in the warehouse to maximise performance in this task. Established industry methods using heuristic approaches require large engineering efforts to optimise for innately variable warehouse configurations. In contrast, multi-agent reinforcement learning (MARL) can be flexibly applied to diverse warehouse configurations (","authors_text":"Aleksandar Krnjaic, Andrew Wing Keung To, Georgios Papoudakis, Jonathan D. Thomas, Kuan-Ho Lao, Lukas Sch\\\"afer, Matthew Haley, Murat Cubuktepe, Peter B\\\"orsting, Raul D. Steleac, Stefano V. Albrecht","cross_cats":["cs.AI","cs.MA","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-22T06:18:41Z","title":"Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.11498","kind":"arxiv","version":3},"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":"sha256:9b74dbd5a759562ab128255092b37e2b5e00dad928520b861729683ee63a55df","target":"record","created_at":"2026-07-05T09:00:54Z","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":"6cc198af4652d3918f7a04ce19bcfc22d698c4911e8773cfd5ab040f9d479877","cross_cats_sorted":["cs.AI","cs.MA","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-22T06:18:41Z","title_canon_sha256":"c9545e781164ecacd1e81e9757538b44fb25a72f67a9dc8312acdbcdeff0a2bc"},"schema_version":"1.0","source":{"id":"2212.11498","kind":"arxiv","version":3}},"canonical_sha256":"e781fb4bb73249f87ba012254e220bcaf411711d1ad80e6d489355d1b82cdd6b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e781fb4bb73249f87ba012254e220bcaf411711d1ad80e6d489355d1b82cdd6b","first_computed_at":"2026-07-05T09:00:54.544605Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:00:54.544605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4Rgo0R8oIQBvWNX3SfdUuQAocQyA79gRTwe+2l7o3MSgo8mv7L5nzCqYEezMuDIg1RYm0ND+esv0CTwHBm0BBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:00:54.545067Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.11498","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9b74dbd5a759562ab128255092b37e2b5e00dad928520b861729683ee63a55df","sha256:f38879c3271e67b1ca4e2c316e2c3d2a85e2fea7eb74100d5a1a0ab230a83bed"],"state_sha256":"0f7aa09b12cbc1bc2a8a46388419e78a1837407a59d74f71a10e477e28930b90"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CwyFbfgv2hkH6cu31jt259umJJjPHyvzgZ19wNVecS08qiIEhTW4s2b+QLJLTsYeLSFHbQLAmVuvBCBmqjo/Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T19:01:51.209910Z","bundle_sha256":"4202760b3d04c7db4d7f756d3b20e5b8b49861c08ae6bc5b4478ca3ca156a955"}}