{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ETACMNQDATGO4JPICTHZ5D4CQ4","short_pith_number":"pith:ETACMNQD","schema_version":"1.0","canonical_sha256":"24c026360304ccee25e814cf9e8f82871372868d6a1db2e232cf64d08b8e38db","source":{"kind":"arxiv","id":"2412.06231","version":1},"attestation_state":"computed","paper":{"title":"A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Billy Pik Lik Lau, Chau Yuen, Leon Fernando, U-Xuan Tan","submitted_at":"2024-12-09T06:08:23Z","abstract_excerpt":"The rapid advancements in unmanned aerial vehicles (UAVs) have unlocked numerous applications, including environmental monitoring, disaster response, and agricultural surveying. Enhancing the collective behavior of multiple decentralized UAVs can significantly improve these applications through more efficient and coordinated operations. In this study, we explore a Recurrent PPO model for target localization in perceptually degraded environments like places without GNSS/GPS signals. We first developed a single-drone approach for target identification, followed by a decentralized two-drone model"},"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":"2412.06231","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-12-09T06:08:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0294c939844ce998131902b55a146a62b9d69c70c02cdc278904e7f05eb4d2cc","abstract_canon_sha256":"42ac60e3aa86292d02ebfaa206c0421b8320898a5a93aa034912befd7098edf9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:17.537754Z","signature_b64":"Kk1P8qFdC/HgT4W2HYk2qNIgLvGT5Pe5ZX/nZlg0QsFbMaxzXU5ASGWNetB1hT1W27k9kQSbWJO+TpFGjNHxBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24c026360304ccee25e814cf9e8f82871372868d6a1db2e232cf64d08b8e38db","last_reissued_at":"2026-07-05T09:46:17.537169Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:17.537169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Billy Pik Lik Lau, Chau Yuen, Leon Fernando, U-Xuan Tan","submitted_at":"2024-12-09T06:08:23Z","abstract_excerpt":"The rapid advancements in unmanned aerial vehicles (UAVs) have unlocked numerous applications, including environmental monitoring, disaster response, and agricultural surveying. Enhancing the collective behavior of multiple decentralized UAVs can significantly improve these applications through more efficient and coordinated operations. In this study, we explore a Recurrent PPO model for target localization in perceptually degraded environments like places without GNSS/GPS signals. We first developed a single-drone approach for target identification, followed by a decentralized two-drone model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.06231","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/2412.06231/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":"2412.06231","created_at":"2026-07-05T09:46:17.537225+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.06231v1","created_at":"2026-07-05T09:46:17.537225+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.06231","created_at":"2026-07-05T09:46:17.537225+00:00"},{"alias_kind":"pith_short_12","alias_value":"ETACMNQDATGO","created_at":"2026-07-05T09:46:17.537225+00:00"},{"alias_kind":"pith_short_16","alias_value":"ETACMNQDATGO4JPI","created_at":"2026-07-05T09:46:17.537225+00:00"},{"alias_kind":"pith_short_8","alias_value":"ETACMNQD","created_at":"2026-07-05T09:46:17.537225+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ETACMNQDATGO4JPICTHZ5D4CQ4","json":"https://pith.science/pith/ETACMNQDATGO4JPICTHZ5D4CQ4.json","graph_json":"https://pith.science/api/pith-number/ETACMNQDATGO4JPICTHZ5D4CQ4/graph.json","events_json":"https://pith.science/api/pith-number/ETACMNQDATGO4JPICTHZ5D4CQ4/events.json","paper":"https://pith.science/paper/ETACMNQD"},"agent_actions":{"view_html":"https://pith.science/pith/ETACMNQDATGO4JPICTHZ5D4CQ4","download_json":"https://pith.science/pith/ETACMNQDATGO4JPICTHZ5D4CQ4.json","view_paper":"https://pith.science/paper/ETACMNQD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.06231&json=true","fetch_graph":"https://pith.science/api/pith-number/ETACMNQDATGO4JPICTHZ5D4CQ4/graph.json","fetch_events":"https://pith.science/api/pith-number/ETACMNQDATGO4JPICTHZ5D4CQ4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ETACMNQDATGO4JPICTHZ5D4CQ4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ETACMNQDATGO4JPICTHZ5D4CQ4/action/storage_attestation","attest_author":"https://pith.science/pith/ETACMNQDATGO4JPICTHZ5D4CQ4/action/author_attestation","sign_citation":"https://pith.science/pith/ETACMNQDATGO4JPICTHZ5D4CQ4/action/citation_signature","submit_replication":"https://pith.science/pith/ETACMNQDATGO4JPICTHZ5D4CQ4/action/replication_record"}},"created_at":"2026-07-05T09:46:17.537225+00:00","updated_at":"2026-07-05T09:46:17.537225+00:00"}