{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:IMHGHOU7GJN3YLNKEKT5FUH6G5","short_pith_number":"pith:IMHGHOU7","canonical_record":{"source":{"id":"2402.04168","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T17:24:06Z","cross_cats_sorted":["cs.CV","cs.RO"],"title_canon_sha256":"f3123225d12ccd3ddab68568a33bbc72ad9b03791c40f06b739e5be7f9e9b8bc","abstract_canon_sha256":"c59b043675db0e1c3836caa18ce7ff2f7ed59b2d5bf1950f62b0268a124f4f12"},"schema_version":"1.0"},"canonical_sha256":"430e63ba9f325bbc2daa22a7d2d0fe374d88bca1c3ba09b67bb3f395aea29719","source":{"kind":"arxiv","id":"2402.04168","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.04168","created_at":"2026-07-05T10:58:14Z"},{"alias_kind":"arxiv_version","alias_value":"2402.04168v2","created_at":"2026-07-05T10:58:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.04168","created_at":"2026-07-05T10:58:14Z"},{"alias_kind":"pith_short_12","alias_value":"IMHGHOU7GJN3","created_at":"2026-07-05T10:58:14Z"},{"alias_kind":"pith_short_16","alias_value":"IMHGHOU7GJN3YLNK","created_at":"2026-07-05T10:58:14Z"},{"alias_kind":"pith_short_8","alias_value":"IMHGHOU7","created_at":"2026-07-05T10:58:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:IMHGHOU7GJN3YLNKEKT5FUH6G5","target":"record","payload":{"canonical_record":{"source":{"id":"2402.04168","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T17:24:06Z","cross_cats_sorted":["cs.CV","cs.RO"],"title_canon_sha256":"f3123225d12ccd3ddab68568a33bbc72ad9b03791c40f06b739e5be7f9e9b8bc","abstract_canon_sha256":"c59b043675db0e1c3836caa18ce7ff2f7ed59b2d5bf1950f62b0268a124f4f12"},"schema_version":"1.0"},"canonical_sha256":"430e63ba9f325bbc2daa22a7d2d0fe374d88bca1c3ba09b67bb3f395aea29719","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:58:14.539613Z","signature_b64":"stjq30Npl3GU3pt0eq1fVBq0i+6/bNT+LcLLgCo9XItD65UlLOzCa5NBDhUlTeCE2Yuj9BJnNKeDqs/Q46NmCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"430e63ba9f325bbc2daa22a7d2d0fe374d88bca1c3ba09b67bb3f395aea29719","last_reissued_at":"2026-07-05T10:58:14.539104Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:58:14.539104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.04168","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:58:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1XtOMGFNIlWBiw2X3zBicjBlaU/Qh27bpozDUBZBeH/e/U6AastWPyk9owv9nKlE94KAc2gE/Du38a9THLNhBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T17:20:46.678447Z"},"content_sha256":"364582c58f836d12f448d52d0c2d37e040698c83ac87860aeb8f6996bc4fb48a","schema_version":"1.0","event_id":"sha256:364582c58f836d12f448d52d0c2d37e040698c83ac87860aeb8f6996bc4fb48a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:IMHGHOU7GJN3YLNKEKT5FUH6G5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.RO"],"primary_cat":"cs.LG","authors_text":"Ahmed Abouelazm, Daniel Bogdoll, Jing Qin, J. Marius Z\\\"ollner, Moritz Nekolla, Tim Joseph","submitted_at":"2024-02-06T17:24:06Z","abstract_excerpt":"Reinforcement Learning is a highly active research field with promising advancements. In the field of autonomous driving, however, often very simple scenarios are being examined. Common approaches use non-interpretable control commands as the action space and unstructured reward designs which lack structure. In this work, we introduce Informed Reinforcement Learning, where a structured rulebook is integrated as a knowledge source. We learn trajectories and asses them with a situation-aware reward design, leading to a dynamic reward which allows the agent to learn situations which require contr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.04168","kind":"arxiv","version":2},"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/2402.04168/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":"2026-07-05T10:58:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aMVsRVz1eEGvlYvVEzYYoElGEllGPcUuogxd1IP6CosF9RUtlFqFqedu22GIyjk5BeedzLSa9F7TLtLxPLu4DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T17:20:46.678960Z"},"content_sha256":"8510d3bfb556e018c124d951384cca5e4da3136122c46db97918f3dbdcda473b","schema_version":"1.0","event_id":"sha256:8510d3bfb556e018c124d951384cca5e4da3136122c46db97918f3dbdcda473b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IMHGHOU7GJN3YLNKEKT5FUH6G5/bundle.json","state_url":"https://pith.science/pith/IMHGHOU7GJN3YLNKEKT5FUH6G5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IMHGHOU7GJN3YLNKEKT5FUH6G5/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":"2026-08-13T17:20:46Z","links":{"resolver":"https://pith.science/pith/IMHGHOU7GJN3YLNKEKT5FUH6G5","bundle":"https://pith.science/pith/IMHGHOU7GJN3YLNKEKT5FUH6G5/bundle.json","state":"https://pith.science/pith/IMHGHOU7GJN3YLNKEKT5FUH6G5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IMHGHOU7GJN3YLNKEKT5FUH6G5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:IMHGHOU7GJN3YLNKEKT5FUH6G5","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":"c59b043675db0e1c3836caa18ce7ff2f7ed59b2d5bf1950f62b0268a124f4f12","cross_cats_sorted":["cs.CV","cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T17:24:06Z","title_canon_sha256":"f3123225d12ccd3ddab68568a33bbc72ad9b03791c40f06b739e5be7f9e9b8bc"},"schema_version":"1.0","source":{"id":"2402.04168","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.04168","created_at":"2026-07-05T10:58:14Z"},{"alias_kind":"arxiv_version","alias_value":"2402.04168v2","created_at":"2026-07-05T10:58:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.04168","created_at":"2026-07-05T10:58:14Z"},{"alias_kind":"pith_short_12","alias_value":"IMHGHOU7GJN3","created_at":"2026-07-05T10:58:14Z"},{"alias_kind":"pith_short_16","alias_value":"IMHGHOU7GJN3YLNK","created_at":"2026-07-05T10:58:14Z"},{"alias_kind":"pith_short_8","alias_value":"IMHGHOU7","created_at":"2026-07-05T10:58:14Z"}],"graph_snapshots":[{"event_id":"sha256:8510d3bfb556e018c124d951384cca5e4da3136122c46db97918f3dbdcda473b","target":"graph","created_at":"2026-07-05T10:58:14Z","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/2402.04168/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reinforcement Learning is a highly active research field with promising advancements. In the field of autonomous driving, however, often very simple scenarios are being examined. Common approaches use non-interpretable control commands as the action space and unstructured reward designs which lack structure. In this work, we introduce Informed Reinforcement Learning, where a structured rulebook is integrated as a knowledge source. We learn trajectories and asses them with a situation-aware reward design, leading to a dynamic reward which allows the agent to learn situations which require contr","authors_text":"Ahmed Abouelazm, Daniel Bogdoll, Jing Qin, J. Marius Z\\\"ollner, Moritz Nekolla, Tim Joseph","cross_cats":["cs.CV","cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T17:24:06Z","title":"Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.04168","kind":"arxiv","version":2},"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":"sha256:364582c58f836d12f448d52d0c2d37e040698c83ac87860aeb8f6996bc4fb48a","target":"record","created_at":"2026-07-05T10:58:14Z","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":"c59b043675db0e1c3836caa18ce7ff2f7ed59b2d5bf1950f62b0268a124f4f12","cross_cats_sorted":["cs.CV","cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T17:24:06Z","title_canon_sha256":"f3123225d12ccd3ddab68568a33bbc72ad9b03791c40f06b739e5be7f9e9b8bc"},"schema_version":"1.0","source":{"id":"2402.04168","kind":"arxiv","version":2}},"canonical_sha256":"430e63ba9f325bbc2daa22a7d2d0fe374d88bca1c3ba09b67bb3f395aea29719","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"430e63ba9f325bbc2daa22a7d2d0fe374d88bca1c3ba09b67bb3f395aea29719","first_computed_at":"2026-07-05T10:58:14.539104Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:58:14.539104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"stjq30Npl3GU3pt0eq1fVBq0i+6/bNT+LcLLgCo9XItD65UlLOzCa5NBDhUlTeCE2Yuj9BJnNKeDqs/Q46NmCA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:58:14.539613Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.04168","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:364582c58f836d12f448d52d0c2d37e040698c83ac87860aeb8f6996bc4fb48a","sha256:8510d3bfb556e018c124d951384cca5e4da3136122c46db97918f3dbdcda473b"],"state_sha256":"037ccf4af9205d8dd8dfa73e378f6beb7afccff946ec9f6eca9cdc2dcff36dab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GY5LxQZYWXvYJFbrQfFj/BXAmM6GfwG6xdYupcqHLo1S5fZ7WWeNPNnpYuVz/vXlY0F7RhChYfWcqDg66ra/CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T17:20:46.691362Z","bundle_sha256":"f1844603e5a38438834b9e948368fd323bafa439b4a59b8be39b2b55e7c10a51"}}