{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:CCQPXCAYS4OPOB3HS2AKNY6E5Q","short_pith_number":"pith:CCQPXCAY","schema_version":"1.0","canonical_sha256":"10a0fb8818971cf707679680a6e3c4ec2f8b2b259f9f0505d19f1e3836be5f9d","source":{"kind":"arxiv","id":"1704.03952","version":4},"attestation_state":"computed","paper":{"title":"Virtual to Real Reinforcement Learning for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Cewu Lu, Xinlei Pan, Yurong You, Ziyan Wang","submitted_at":"2017-04-13T00:03:40Z","abstract_excerpt":"Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then transfer to the real environment. In this paper, we propose a novel realistic translation network to make model trained in virtual environment be workable in real world. The proposed network can convert non-realistic virtual image input into a realistic one with similar scene structure. Given realistic f"},"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":"1704.03952","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-04-13T00:03:40Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e754e2292f5bc6cc4daf7f0fec64e035f9a052614d93e61b51ac0393a39b66f8","abstract_canon_sha256":"29e9e49b1a1e2b147b4da7366c082b860a28a1e3d45683105b039cce963ad493"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:22.712346Z","signature_b64":"EACOaHQ6DK8GmcIOTzvOXJzyCQe43dhlma6tWb/nKMUjaWc9GYhkDUUZifhKEP19OJdPgkWCz/dytdzcW0pmCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10a0fb8818971cf707679680a6e3c4ec2f8b2b259f9f0505d19f1e3836be5f9d","last_reissued_at":"2026-05-18T00:34:22.711889Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:22.711889Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Virtual to Real Reinforcement Learning for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Cewu Lu, Xinlei Pan, Yurong You, Ziyan Wang","submitted_at":"2017-04-13T00:03:40Z","abstract_excerpt":"Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then transfer to the real environment. In this paper, we propose a novel realistic translation network to make model trained in virtual environment be workable in real world. The proposed network can convert non-realistic virtual image input into a realistic one with similar scene structure. Given realistic f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.03952","kind":"arxiv","version":4},"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":"1704.03952","created_at":"2026-05-18T00:34:22.711954+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.03952v4","created_at":"2026-05-18T00:34:22.711954+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.03952","created_at":"2026-05-18T00:34:22.711954+00:00"},{"alias_kind":"pith_short_12","alias_value":"CCQPXCAYS4OP","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_16","alias_value":"CCQPXCAYS4OPOB3H","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_8","alias_value":"CCQPXCAY","created_at":"2026-05-18T12:31:10.602751+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.02548","citing_title":"Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Models","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CCQPXCAYS4OPOB3HS2AKNY6E5Q","json":"https://pith.science/pith/CCQPXCAYS4OPOB3HS2AKNY6E5Q.json","graph_json":"https://pith.science/api/pith-number/CCQPXCAYS4OPOB3HS2AKNY6E5Q/graph.json","events_json":"https://pith.science/api/pith-number/CCQPXCAYS4OPOB3HS2AKNY6E5Q/events.json","paper":"https://pith.science/paper/CCQPXCAY"},"agent_actions":{"view_html":"https://pith.science/pith/CCQPXCAYS4OPOB3HS2AKNY6E5Q","download_json":"https://pith.science/pith/CCQPXCAYS4OPOB3HS2AKNY6E5Q.json","view_paper":"https://pith.science/paper/CCQPXCAY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.03952&json=true","fetch_graph":"https://pith.science/api/pith-number/CCQPXCAYS4OPOB3HS2AKNY6E5Q/graph.json","fetch_events":"https://pith.science/api/pith-number/CCQPXCAYS4OPOB3HS2AKNY6E5Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CCQPXCAYS4OPOB3HS2AKNY6E5Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CCQPXCAYS4OPOB3HS2AKNY6E5Q/action/storage_attestation","attest_author":"https://pith.science/pith/CCQPXCAYS4OPOB3HS2AKNY6E5Q/action/author_attestation","sign_citation":"https://pith.science/pith/CCQPXCAYS4OPOB3HS2AKNY6E5Q/action/citation_signature","submit_replication":"https://pith.science/pith/CCQPXCAYS4OPOB3HS2AKNY6E5Q/action/replication_record"}},"created_at":"2026-05-18T00:34:22.711954+00:00","updated_at":"2026-05-18T00:34:22.711954+00:00"}