{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:J7WUJQT3GHP5WQTYSVIG6J7HOJ","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":"42e16a6266b1c0e816410049c14685ba04700b139e21743e5abe759d63fa4565","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-11-27T16:08:32Z","title_canon_sha256":"cbb3a8949679e6949ce0ba6c37f85b3e24d23469b93ac2ee354bf933720f7fa5"},"schema_version":"1.0","source":{"id":"2211.14874","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.14874","created_at":"2026-07-05T05:19:44Z"},{"alias_kind":"arxiv_version","alias_value":"2211.14874v1","created_at":"2026-07-05T05:19:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.14874","created_at":"2026-07-05T05:19:44Z"},{"alias_kind":"pith_short_12","alias_value":"J7WUJQT3GHP5","created_at":"2026-07-05T05:19:44Z"},{"alias_kind":"pith_short_16","alias_value":"J7WUJQT3GHP5WQTY","created_at":"2026-07-05T05:19:44Z"},{"alias_kind":"pith_short_8","alias_value":"J7WUJQT3","created_at":"2026-07-05T05:19:44Z"}],"graph_snapshots":[{"event_id":"sha256:c25f9815410561329f6f3b36a638e1e7588d52267a519539b79468d7fd653e70","target":"graph","created_at":"2026-07-05T05:19:44Z","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/2211.14874/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reinforcement learning (RL) is a promising solution for autonomous vehicles to deal with complex and uncertain traffic environments. The RL training process is however expensive, unsafe, and time consuming. Algorithms are often developed first in simulation and then transferred to the real world, leading to a common sim2real challenge that performance decreases when the domain changes. In this paper, we propose a transfer learning process to minimize the gap by exploiting digital twin technology, relying on a systematic and simultaneous combination of virtual and real world data coming from ve","authors_text":"Javier Alonso-Mora, Jean Pierre Allamaa, Kevin Voogd, Tong Duy Son","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-11-27T16:08:32Z","title":"Reinforcement Learning from Simulation to Real World Autonomous Driving using Digital Twin"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.14874","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":"sha256:1fefb6fe18137771b785aed9473b602efa6bc97ad547ef187d4b17a61e45b321","target":"record","created_at":"2026-07-05T05:19:44Z","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":"42e16a6266b1c0e816410049c14685ba04700b139e21743e5abe759d63fa4565","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-11-27T16:08:32Z","title_canon_sha256":"cbb3a8949679e6949ce0ba6c37f85b3e24d23469b93ac2ee354bf933720f7fa5"},"schema_version":"1.0","source":{"id":"2211.14874","kind":"arxiv","version":1}},"canonical_sha256":"4fed44c27b31dfdb427895506f27e7726f044eef1a4837aa8f193874f416baaa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4fed44c27b31dfdb427895506f27e7726f044eef1a4837aa8f193874f416baaa","first_computed_at":"2026-07-05T05:19:44.754777Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:19:44.754777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GS6ZJMti4KqYu6EaecgpCBh7RfJKZnnP3J1K6tNifq0qafKJdjH+DaiLUaYeG6vnhHVRkrZr242FrsMYgdfgAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:19:44.755259Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.14874","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1fefb6fe18137771b785aed9473b602efa6bc97ad547ef187d4b17a61e45b321","sha256:c25f9815410561329f6f3b36a638e1e7588d52267a519539b79468d7fd653e70"],"state_sha256":"62184aaf68308e3b453ffa275644875bbcc0d3c48859b26420e4e1c51629d28f"}