{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZGXIPSXTKG7FHUVPHLZQXGKZSB","short_pith_number":"pith:ZGXIPSXT","schema_version":"1.0","canonical_sha256":"c9ae87caf351be53d2af3af30b9959905c1c8fff66c876520d00d382bef0ae9b","source":{"kind":"arxiv","id":"1812.09724","version":1},"attestation_state":"computed","paper":{"title":"Parallelized Interactive Machine Learning on Autonomous Vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.LG","authors_text":"Caylin Hickey, Xi Chen","submitted_at":"2018-12-23T14:57:28Z","abstract_excerpt":"Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by learning directly from image input. A deep neural network is used as a function approximator and requires no specific state information. However, one drawback of using only images as input is that this approach requires a prohibitively large amount of training time and data for the model to learn the state feature representation and approach reasonable performance. This is not feasible in real-world applications, especially when the data are expansive and training phase could introduce disast"},"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":"1812.09724","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-23T14:57:28Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"6f85a0b278a91841b9989fc25059c7b63065ea2b0fbce1c0acba61adb3dc78c7","abstract_canon_sha256":"41d2ae3fdd6e32ba6048b27cac41e7d8346d96da1e984f98b17c070000a152bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:26.982004Z","signature_b64":"dN4tGSCjh8eQ+4kUSkgH55EiYm6LyDgCUjZTDkgz1lKG+vNWRKP9Ma26Shq9URJfbxui1pi6KQYXQ5TwgNH4CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9ae87caf351be53d2af3af30b9959905c1c8fff66c876520d00d382bef0ae9b","last_reissued_at":"2026-05-17T23:57:26.981211Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:26.981211Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parallelized Interactive Machine Learning on Autonomous Vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.LG","authors_text":"Caylin Hickey, Xi Chen","submitted_at":"2018-12-23T14:57:28Z","abstract_excerpt":"Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by learning directly from image input. A deep neural network is used as a function approximator and requires no specific state information. However, one drawback of using only images as input is that this approach requires a prohibitively large amount of training time and data for the model to learn the state feature representation and approach reasonable performance. This is not feasible in real-world applications, especially when the data are expansive and training phase could introduce disast"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.09724","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":""},"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":"1812.09724","created_at":"2026-05-17T23:57:26.981356+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.09724v1","created_at":"2026-05-17T23:57:26.981356+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.09724","created_at":"2026-05-17T23:57:26.981356+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZGXIPSXTKG7F","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZGXIPSXTKG7FHUVP","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZGXIPSXT","created_at":"2026-05-18T12:33:07.085635+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/ZGXIPSXTKG7FHUVPHLZQXGKZSB","json":"https://pith.science/pith/ZGXIPSXTKG7FHUVPHLZQXGKZSB.json","graph_json":"https://pith.science/api/pith-number/ZGXIPSXTKG7FHUVPHLZQXGKZSB/graph.json","events_json":"https://pith.science/api/pith-number/ZGXIPSXTKG7FHUVPHLZQXGKZSB/events.json","paper":"https://pith.science/paper/ZGXIPSXT"},"agent_actions":{"view_html":"https://pith.science/pith/ZGXIPSXTKG7FHUVPHLZQXGKZSB","download_json":"https://pith.science/pith/ZGXIPSXTKG7FHUVPHLZQXGKZSB.json","view_paper":"https://pith.science/paper/ZGXIPSXT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.09724&json=true","fetch_graph":"https://pith.science/api/pith-number/ZGXIPSXTKG7FHUVPHLZQXGKZSB/graph.json","fetch_events":"https://pith.science/api/pith-number/ZGXIPSXTKG7FHUVPHLZQXGKZSB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZGXIPSXTKG7FHUVPHLZQXGKZSB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZGXIPSXTKG7FHUVPHLZQXGKZSB/action/storage_attestation","attest_author":"https://pith.science/pith/ZGXIPSXTKG7FHUVPHLZQXGKZSB/action/author_attestation","sign_citation":"https://pith.science/pith/ZGXIPSXTKG7FHUVPHLZQXGKZSB/action/citation_signature","submit_replication":"https://pith.science/pith/ZGXIPSXTKG7FHUVPHLZQXGKZSB/action/replication_record"}},"created_at":"2026-05-17T23:57:26.981356+00:00","updated_at":"2026-05-17T23:57:26.981356+00:00"}