{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:DEMHT22W3XDH5TXLAVWZN3GPD7","short_pith_number":"pith:DEMHT22W","canonical_record":{"source":{"id":"2003.10923","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-03-21T19:33:00Z","cross_cats_sorted":["cs.AI","cs.LG","eess.SP"],"title_canon_sha256":"62640d2848b2bbd69fe2d62a81d5784c9e0afc1e41a60d0814f9308dccd61663","abstract_canon_sha256":"e2b7de25827fdcb36ac719ef52f99922077303b94bf06514e8cc0adfc10408eb"},"schema_version":"1.0"},"canonical_sha256":"191879eb56ddc67eceeb056d96eccf1ff41298ae207eab37ccc421eb68f61e67","source":{"kind":"arxiv","id":"2003.10923","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.10923","created_at":"2026-07-05T00:50:21Z"},{"alias_kind":"arxiv_version","alias_value":"2003.10923v1","created_at":"2026-07-05T00:50:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.10923","created_at":"2026-07-05T00:50:21Z"},{"alias_kind":"pith_short_12","alias_value":"DEMHT22W3XDH","created_at":"2026-07-05T00:50:21Z"},{"alias_kind":"pith_short_16","alias_value":"DEMHT22W3XDH5TXL","created_at":"2026-07-05T00:50:21Z"},{"alias_kind":"pith_short_8","alias_value":"DEMHT22W","created_at":"2026-07-05T00:50:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:DEMHT22W3XDH5TXLAVWZN3GPD7","target":"record","payload":{"canonical_record":{"source":{"id":"2003.10923","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-03-21T19:33:00Z","cross_cats_sorted":["cs.AI","cs.LG","eess.SP"],"title_canon_sha256":"62640d2848b2bbd69fe2d62a81d5784c9e0afc1e41a60d0814f9308dccd61663","abstract_canon_sha256":"e2b7de25827fdcb36ac719ef52f99922077303b94bf06514e8cc0adfc10408eb"},"schema_version":"1.0"},"canonical_sha256":"191879eb56ddc67eceeb056d96eccf1ff41298ae207eab37ccc421eb68f61e67","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:50:21.918074Z","signature_b64":"OmBYp3fYVc7ZaJ26WGEf9UPF2hxaqISJpyiAJkQeV+QjC/ften+r3jFoLgt7TM/nKS2xmPppqw6eS2QM++lPDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"191879eb56ddc67eceeb056d96eccf1ff41298ae207eab37ccc421eb68f61e67","last_reissued_at":"2026-07-05T00:50:21.917634Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:50:21.917634Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2003.10923","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":"2026-07-05T00:50:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PHbQuaGpMxu005h39YIccKDY75oGPKwJ68eg0Yyli8cafLwZQTCp0y9GlK4ehIguep2kAZLvgfnZ9YDdNEaFCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T09:05:49.829256Z"},"content_sha256":"51a9bcd4d3ce8c1a41be6040ce55ed02e4f3de291a6265222ae439d0370fef8d","schema_version":"1.0","event_id":"sha256:51a9bcd4d3ce8c1a41be6040ce55ed02e4f3de291a6265222ae439d0370fef8d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:DEMHT22W3XDH5TXLAVWZN3GPD7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","eess.SP"],"primary_cat":"cs.RO","authors_text":"Hakim Ghazzai, Hichem Besbes, Omar Bouhamed, Yehia Massoud","submitted_at":"2020-03-21T19:33:00Z","abstract_excerpt":"In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous action space is designed to train the UAV to navigate through or over the obstacles to reach its assigned target. A customized reward function is developed to minimize the distance separating the UAV and its destination while penalizing collis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.10923","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/2003.10923/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":"2026-07-05T00:50:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6YGoWg4Qb6GIukS5jHbFXcMEKN9boBi7J1qpTegPi4tWArF4Y4uU30T5WXWDKSetfedIFdWOC6EQ6hrOaqb/Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T09:05:49.829809Z"},"content_sha256":"aac41370f828d7b89e6cdcb92d177183bda8a2d5521578418821b109a90dba24","schema_version":"1.0","event_id":"sha256:aac41370f828d7b89e6cdcb92d177183bda8a2d5521578418821b109a90dba24"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DEMHT22W3XDH5TXLAVWZN3GPD7/bundle.json","state_url":"https://pith.science/pith/DEMHT22W3XDH5TXLAVWZN3GPD7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DEMHT22W3XDH5TXLAVWZN3GPD7/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":"2026-08-04T09:05:49Z","links":{"resolver":"https://pith.science/pith/DEMHT22W3XDH5TXLAVWZN3GPD7","bundle":"https://pith.science/pith/DEMHT22W3XDH5TXLAVWZN3GPD7/bundle.json","state":"https://pith.science/pith/DEMHT22W3XDH5TXLAVWZN3GPD7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DEMHT22W3XDH5TXLAVWZN3GPD7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:DEMHT22W3XDH5TXLAVWZN3GPD7","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":"e2b7de25827fdcb36ac719ef52f99922077303b94bf06514e8cc0adfc10408eb","cross_cats_sorted":["cs.AI","cs.LG","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-03-21T19:33:00Z","title_canon_sha256":"62640d2848b2bbd69fe2d62a81d5784c9e0afc1e41a60d0814f9308dccd61663"},"schema_version":"1.0","source":{"id":"2003.10923","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.10923","created_at":"2026-07-05T00:50:21Z"},{"alias_kind":"arxiv_version","alias_value":"2003.10923v1","created_at":"2026-07-05T00:50:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.10923","created_at":"2026-07-05T00:50:21Z"},{"alias_kind":"pith_short_12","alias_value":"DEMHT22W3XDH","created_at":"2026-07-05T00:50:21Z"},{"alias_kind":"pith_short_16","alias_value":"DEMHT22W3XDH5TXL","created_at":"2026-07-05T00:50:21Z"},{"alias_kind":"pith_short_8","alias_value":"DEMHT22W","created_at":"2026-07-05T00:50:21Z"}],"graph_snapshots":[{"event_id":"sha256:aac41370f828d7b89e6cdcb92d177183bda8a2d5521578418821b109a90dba24","target":"graph","created_at":"2026-07-05T00:50:21Z","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/2003.10923/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous action space is designed to train the UAV to navigate through or over the obstacles to reach its assigned target. A customized reward function is developed to minimize the distance separating the UAV and its destination while penalizing collis","authors_text":"Hakim Ghazzai, Hichem Besbes, Omar Bouhamed, Yehia Massoud","cross_cats":["cs.AI","cs.LG","eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-03-21T19:33:00Z","title":"Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.10923","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":"sha256:51a9bcd4d3ce8c1a41be6040ce55ed02e4f3de291a6265222ae439d0370fef8d","target":"record","created_at":"2026-07-05T00:50:21Z","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":"e2b7de25827fdcb36ac719ef52f99922077303b94bf06514e8cc0adfc10408eb","cross_cats_sorted":["cs.AI","cs.LG","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-03-21T19:33:00Z","title_canon_sha256":"62640d2848b2bbd69fe2d62a81d5784c9e0afc1e41a60d0814f9308dccd61663"},"schema_version":"1.0","source":{"id":"2003.10923","kind":"arxiv","version":1}},"canonical_sha256":"191879eb56ddc67eceeb056d96eccf1ff41298ae207eab37ccc421eb68f61e67","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"191879eb56ddc67eceeb056d96eccf1ff41298ae207eab37ccc421eb68f61e67","first_computed_at":"2026-07-05T00:50:21.917634Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:50:21.917634Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OmBYp3fYVc7ZaJ26WGEf9UPF2hxaqISJpyiAJkQeV+QjC/ften+r3jFoLgt7TM/nKS2xmPppqw6eS2QM++lPDg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:50:21.918074Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.10923","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:51a9bcd4d3ce8c1a41be6040ce55ed02e4f3de291a6265222ae439d0370fef8d","sha256:aac41370f828d7b89e6cdcb92d177183bda8a2d5521578418821b109a90dba24"],"state_sha256":"8042d33e5f9fef6e5191a72b29f4873800c734e3737a67d9d39fb739f417611d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o2TE41DyyTIG8i0ZgrVzAn/KkiqyBMFsx0Eb5yvVgqm4OhftqwUagDKBdjeeFYIc9ShidC0CiNpxu1jGUO8cAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T09:05:49.833897Z","bundle_sha256":"fc42306812aec1b40786d56b568ff45621b944ad7ee4afe3dcb4075f49fe8478"}}