{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:B6QNPTVAHPS3CUZPSIDT5XROA5","short_pith_number":"pith:B6QNPTVA","canonical_record":{"source":{"id":"2204.09603","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-20T16:33:01Z","cross_cats_sorted":["cs.AI","math.OC"],"title_canon_sha256":"7d51464451b6d5b0e6249811d2ccb3d556b33b5bac8f609267e5e501c8fd0185","abstract_canon_sha256":"ca67d07a4bc386447ba9c1d49015801fa09ae6ef7ad676491ecc7eb1aea61e58"},"schema_version":"1.0"},"canonical_sha256":"0fa0d7cea03be5b1532f92073ede2e07566392fe21fe6f142512e5a959187866","source":{"kind":"arxiv","id":"2204.09603","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.09603","created_at":"2026-07-05T09:57:17Z"},{"alias_kind":"arxiv_version","alias_value":"2204.09603v3","created_at":"2026-07-05T09:57:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.09603","created_at":"2026-07-05T09:57:17Z"},{"alias_kind":"pith_short_12","alias_value":"B6QNPTVAHPS3","created_at":"2026-07-05T09:57:17Z"},{"alias_kind":"pith_short_16","alias_value":"B6QNPTVAHPS3CUZP","created_at":"2026-07-05T09:57:17Z"},{"alias_kind":"pith_short_8","alias_value":"B6QNPTVA","created_at":"2026-07-05T09:57:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:B6QNPTVAHPS3CUZPSIDT5XROA5","target":"record","payload":{"canonical_record":{"source":{"id":"2204.09603","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-20T16:33:01Z","cross_cats_sorted":["cs.AI","math.OC"],"title_canon_sha256":"7d51464451b6d5b0e6249811d2ccb3d556b33b5bac8f609267e5e501c8fd0185","abstract_canon_sha256":"ca67d07a4bc386447ba9c1d49015801fa09ae6ef7ad676491ecc7eb1aea61e58"},"schema_version":"1.0"},"canonical_sha256":"0fa0d7cea03be5b1532f92073ede2e07566392fe21fe6f142512e5a959187866","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:57:17.613364Z","signature_b64":"TEhXwPrrWS3iw7CoAZbGW9pp7lcjJqr4uYAljnrmPthnqTHU7cSYrnVdADoEht54U7aL0gmKtecRTbhORfxNDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0fa0d7cea03be5b1532f92073ede2e07566392fe21fe6f142512e5a959187866","last_reissued_at":"2026-07-05T09:57:17.612831Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:57:17.612831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.09603","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":"2026-07-05T09:57:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cF0qyiYSSmuhD9bc/CSj5yJZYa0XlQSwZZ2U/+AbynU6ZK4BadzSo5+kJtgZFE4pA2gj6THHEBftJFplpcq+Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T05:50:29.337340Z"},"content_sha256":"3152575bfaed3c2c9592feb6bfa4d9b07bd6f30601d9ac29dfdb31c36e7e284f","schema_version":"1.0","event_id":"sha256:3152575bfaed3c2c9592feb6bfa4d9b07bd6f30601d9ac29dfdb31c36e7e284f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:B6QNPTVAHPS3CUZPSIDT5XROA5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","math.OC"],"primary_cat":"cs.LG","authors_text":"Fabio Stella, Francesco Stranieri","submitted_at":"2022-04-20T16:33:01Z","abstract_excerpt":"In this study, we analyze and compare the performance of state-of-the-art deep reinforcement learning algorithms for solving the supply chain inventory management problem. This complex sequential decision-making problem consists of determining the optimal quantity of products to be produced and shipped across different warehouses over a given time horizon. In particular, we present a mathematical formulation of a two-echelon supply chain environment with stochastic and seasonal demand, which allows managing an arbitrary number of warehouses and product types. Through a rich set of numerical ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.09603","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/2204.09603/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":"2026-07-05T09:57:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2wJzuNztCzG0rzFH1Wfj/68JQdIdFXuMzJ9ECLmBB+MlObRiqpHoB0Txva+9Ke+DbMLLUL0Zy/7UBdMifCGGAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T05:50:29.338340Z"},"content_sha256":"0da3a20323a5dc7c2f20a995d22aae61031f035910889e2bc6b5872905d5d3a1","schema_version":"1.0","event_id":"sha256:0da3a20323a5dc7c2f20a995d22aae61031f035910889e2bc6b5872905d5d3a1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B6QNPTVAHPS3CUZPSIDT5XROA5/bundle.json","state_url":"https://pith.science/pith/B6QNPTVAHPS3CUZPSIDT5XROA5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B6QNPTVAHPS3CUZPSIDT5XROA5/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":"2026-08-12T05:50:29Z","links":{"resolver":"https://pith.science/pith/B6QNPTVAHPS3CUZPSIDT5XROA5","bundle":"https://pith.science/pith/B6QNPTVAHPS3CUZPSIDT5XROA5/bundle.json","state":"https://pith.science/pith/B6QNPTVAHPS3CUZPSIDT5XROA5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B6QNPTVAHPS3CUZPSIDT5XROA5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:B6QNPTVAHPS3CUZPSIDT5XROA5","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":"ca67d07a4bc386447ba9c1d49015801fa09ae6ef7ad676491ecc7eb1aea61e58","cross_cats_sorted":["cs.AI","math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-20T16:33:01Z","title_canon_sha256":"7d51464451b6d5b0e6249811d2ccb3d556b33b5bac8f609267e5e501c8fd0185"},"schema_version":"1.0","source":{"id":"2204.09603","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.09603","created_at":"2026-07-05T09:57:17Z"},{"alias_kind":"arxiv_version","alias_value":"2204.09603v3","created_at":"2026-07-05T09:57:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.09603","created_at":"2026-07-05T09:57:17Z"},{"alias_kind":"pith_short_12","alias_value":"B6QNPTVAHPS3","created_at":"2026-07-05T09:57:17Z"},{"alias_kind":"pith_short_16","alias_value":"B6QNPTVAHPS3CUZP","created_at":"2026-07-05T09:57:17Z"},{"alias_kind":"pith_short_8","alias_value":"B6QNPTVA","created_at":"2026-07-05T09:57:17Z"}],"graph_snapshots":[{"event_id":"sha256:0da3a20323a5dc7c2f20a995d22aae61031f035910889e2bc6b5872905d5d3a1","target":"graph","created_at":"2026-07-05T09:57:17Z","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/2204.09603/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this study, we analyze and compare the performance of state-of-the-art deep reinforcement learning algorithms for solving the supply chain inventory management problem. This complex sequential decision-making problem consists of determining the optimal quantity of products to be produced and shipped across different warehouses over a given time horizon. In particular, we present a mathematical formulation of a two-echelon supply chain environment with stochastic and seasonal demand, which allows managing an arbitrary number of warehouses and product types. Through a rich set of numerical ex","authors_text":"Fabio Stella, Francesco Stranieri","cross_cats":["cs.AI","math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-20T16:33:01Z","title":"Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.09603","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":"sha256:3152575bfaed3c2c9592feb6bfa4d9b07bd6f30601d9ac29dfdb31c36e7e284f","target":"record","created_at":"2026-07-05T09:57:17Z","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":"ca67d07a4bc386447ba9c1d49015801fa09ae6ef7ad676491ecc7eb1aea61e58","cross_cats_sorted":["cs.AI","math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-20T16:33:01Z","title_canon_sha256":"7d51464451b6d5b0e6249811d2ccb3d556b33b5bac8f609267e5e501c8fd0185"},"schema_version":"1.0","source":{"id":"2204.09603","kind":"arxiv","version":3}},"canonical_sha256":"0fa0d7cea03be5b1532f92073ede2e07566392fe21fe6f142512e5a959187866","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0fa0d7cea03be5b1532f92073ede2e07566392fe21fe6f142512e5a959187866","first_computed_at":"2026-07-05T09:57:17.612831Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:57:17.612831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TEhXwPrrWS3iw7CoAZbGW9pp7lcjJqr4uYAljnrmPthnqTHU7cSYrnVdADoEht54U7aL0gmKtecRTbhORfxNDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:57:17.613364Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.09603","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3152575bfaed3c2c9592feb6bfa4d9b07bd6f30601d9ac29dfdb31c36e7e284f","sha256:0da3a20323a5dc7c2f20a995d22aae61031f035910889e2bc6b5872905d5d3a1"],"state_sha256":"2ad17831999e81bf5fed52b5e526c5fdc7a6547e02526351771b55615b9697b6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b8yb3H9YayF6YRCQM23q6x6GQmteyLE6ZYE32zMcv4Cmevo8nApAhA6bMvlqHCIe9gsP4TLrjyqcBO7kQ0a6DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T05:50:29.344994Z","bundle_sha256":"9d95b37696bda9ca8591fa787287279ba4437b6c4df49b8918079de5c8641701"}}