{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IZABN7VHKDIYDLGPBSOUFH535P","short_pith_number":"pith:IZABN7VH","canonical_record":{"source":{"id":"2507.18795","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-07-24T20:32:47Z","cross_cats_sorted":[],"title_canon_sha256":"335f6012420c8ae15fd74d77fd94205df1fd9e9be595cbf741fbfcbf1ee103e0","abstract_canon_sha256":"c544f0a5041a5d9dc8d70a28893f7e9189b1798ec2bc04466b640d886b957053"},"schema_version":"1.0"},"canonical_sha256":"464016fea750d181accf0c9d429fbbebe9e3213f68979fa325249435515b3fad","source":{"kind":"arxiv","id":"2507.18795","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.18795","created_at":"2026-07-05T11:42:58Z"},{"alias_kind":"arxiv_version","alias_value":"2507.18795v1","created_at":"2026-07-05T11:42:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.18795","created_at":"2026-07-05T11:42:58Z"},{"alias_kind":"pith_short_12","alias_value":"IZABN7VHKDIY","created_at":"2026-07-05T11:42:58Z"},{"alias_kind":"pith_short_16","alias_value":"IZABN7VHKDIYDLGP","created_at":"2026-07-05T11:42:58Z"},{"alias_kind":"pith_short_8","alias_value":"IZABN7VH","created_at":"2026-07-05T11:42:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IZABN7VHKDIYDLGPBSOUFH535P","target":"record","payload":{"canonical_record":{"source":{"id":"2507.18795","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-07-24T20:32:47Z","cross_cats_sorted":[],"title_canon_sha256":"335f6012420c8ae15fd74d77fd94205df1fd9e9be595cbf741fbfcbf1ee103e0","abstract_canon_sha256":"c544f0a5041a5d9dc8d70a28893f7e9189b1798ec2bc04466b640d886b957053"},"schema_version":"1.0"},"canonical_sha256":"464016fea750d181accf0c9d429fbbebe9e3213f68979fa325249435515b3fad","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:58.399264Z","signature_b64":"9QqvazoPxq1gK6xGs7BNvnGmkA3Rj3+lAZMYd+6MjrIFvORGISTDGvX2vBLL4SqG4CYUO/0EKG/NyFhHAG8CCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"464016fea750d181accf0c9d429fbbebe9e3213f68979fa325249435515b3fad","last_reissued_at":"2026-07-05T11:42:58.398825Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:58.398825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.18795","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":"2026-07-05T11:42:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t2j5wHGtXmDisRibvFhcavbx00S6zr8vtksCZjoMb5PMFQBTlk68E6IUsSz0/mAcgmgBxVWO4z/8q2ZMHvqvCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T12:03:10.902236Z"},"content_sha256":"e013b0dc7b9f22c3310df7442d2a214a9db8dd55d972a2022974eea4bd13b474","schema_version":"1.0","event_id":"sha256:e013b0dc7b9f22c3310df7442d2a214a9db8dd55d972a2022974eea4bd13b474"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IZABN7VHKDIYDLGPBSOUFH535P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Simulation-Driven Reinforcement Learning in Queuing Network Routing Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Aaron Ong, Fatima Al-Ani, Jevon Charles, Joshua Forday, Molly Wang, Vinayak Modi","submitted_at":"2025-07-24T20:32:47Z","abstract_excerpt":"This study focuses on the development of a simulation-driven reinforcement learning (RL) framework for optimizing routing decisions in complex queueing network systems, with a particular emphasis on manufacturing and communication applications. Recognizing the limitations of traditional queueing methods, which often struggle with dynamic, uncertain environments, we propose a robust RL approach leveraging Deep Deterministic Policy Gradient (DDPG) combined with Dyna-style planning (Dyna-DDPG). The framework includes a flexible and configurable simulation environment capable of modeling diverse q"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.18795","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/2507.18795/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":"2026-07-05T11:42:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FhDLt5Lpk6a3IdKFBJ0OySgXYIWjA9kV3zS90B5V5gSKiskodU4f6QnMfgQ+7oT6H7l2J3cMW09LHoONgOSWBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T12:03:10.902766Z"},"content_sha256":"29826ded34723ff95691c0dfca832a62cc5e67186852683999214f1032e82e24","schema_version":"1.0","event_id":"sha256:29826ded34723ff95691c0dfca832a62cc5e67186852683999214f1032e82e24"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IZABN7VHKDIYDLGPBSOUFH535P/bundle.json","state_url":"https://pith.science/pith/IZABN7VHKDIYDLGPBSOUFH535P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IZABN7VHKDIYDLGPBSOUFH535P/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":"2026-08-09T12:03:10Z","links":{"resolver":"https://pith.science/pith/IZABN7VHKDIYDLGPBSOUFH535P","bundle":"https://pith.science/pith/IZABN7VHKDIYDLGPBSOUFH535P/bundle.json","state":"https://pith.science/pith/IZABN7VHKDIYDLGPBSOUFH535P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IZABN7VHKDIYDLGPBSOUFH535P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IZABN7VHKDIYDLGPBSOUFH535P","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":"c544f0a5041a5d9dc8d70a28893f7e9189b1798ec2bc04466b640d886b957053","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-07-24T20:32:47Z","title_canon_sha256":"335f6012420c8ae15fd74d77fd94205df1fd9e9be595cbf741fbfcbf1ee103e0"},"schema_version":"1.0","source":{"id":"2507.18795","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.18795","created_at":"2026-07-05T11:42:58Z"},{"alias_kind":"arxiv_version","alias_value":"2507.18795v1","created_at":"2026-07-05T11:42:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.18795","created_at":"2026-07-05T11:42:58Z"},{"alias_kind":"pith_short_12","alias_value":"IZABN7VHKDIY","created_at":"2026-07-05T11:42:58Z"},{"alias_kind":"pith_short_16","alias_value":"IZABN7VHKDIYDLGP","created_at":"2026-07-05T11:42:58Z"},{"alias_kind":"pith_short_8","alias_value":"IZABN7VH","created_at":"2026-07-05T11:42:58Z"}],"graph_snapshots":[{"event_id":"sha256:29826ded34723ff95691c0dfca832a62cc5e67186852683999214f1032e82e24","target":"graph","created_at":"2026-07-05T11:42:58Z","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/2507.18795/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study focuses on the development of a simulation-driven reinforcement learning (RL) framework for optimizing routing decisions in complex queueing network systems, with a particular emphasis on manufacturing and communication applications. Recognizing the limitations of traditional queueing methods, which often struggle with dynamic, uncertain environments, we propose a robust RL approach leveraging Deep Deterministic Policy Gradient (DDPG) combined with Dyna-style planning (Dyna-DDPG). The framework includes a flexible and configurable simulation environment capable of modeling diverse q","authors_text":"Aaron Ong, Fatima Al-Ani, Jevon Charles, Joshua Forday, Molly Wang, Vinayak Modi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-07-24T20:32:47Z","title":"Simulation-Driven Reinforcement Learning in Queuing Network Routing Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.18795","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:e013b0dc7b9f22c3310df7442d2a214a9db8dd55d972a2022974eea4bd13b474","target":"record","created_at":"2026-07-05T11:42:58Z","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":"c544f0a5041a5d9dc8d70a28893f7e9189b1798ec2bc04466b640d886b957053","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-07-24T20:32:47Z","title_canon_sha256":"335f6012420c8ae15fd74d77fd94205df1fd9e9be595cbf741fbfcbf1ee103e0"},"schema_version":"1.0","source":{"id":"2507.18795","kind":"arxiv","version":1}},"canonical_sha256":"464016fea750d181accf0c9d429fbbebe9e3213f68979fa325249435515b3fad","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"464016fea750d181accf0c9d429fbbebe9e3213f68979fa325249435515b3fad","first_computed_at":"2026-07-05T11:42:58.398825Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:42:58.398825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9QqvazoPxq1gK6xGs7BNvnGmkA3Rj3+lAZMYd+6MjrIFvORGISTDGvX2vBLL4SqG4CYUO/0EKG/NyFhHAG8CCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:42:58.399264Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.18795","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e013b0dc7b9f22c3310df7442d2a214a9db8dd55d972a2022974eea4bd13b474","sha256:29826ded34723ff95691c0dfca832a62cc5e67186852683999214f1032e82e24"],"state_sha256":"03997210324a388553412138fcdbee2cdc9ec217766d9515ad1388980bc1d80f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cOQ7YtjSbxEDQ7eEmJaHoNDgjZbzTh3ODqRBP0yVXNNHdn0ckIGY36fummg9psR2hOl5SSpM0w6pY0xZiQPvCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T12:03:10.907801Z","bundle_sha256":"399286770454c39e44c3dafb13c86878a9386ee9e813055a5b231fd5b6f4c39f"}}