{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NXTPMZOMFGMBWAVPUKT2IX5QVS","short_pith_number":"pith:NXTPMZOM","schema_version":"1.0","canonical_sha256":"6de6f665cc29981b02afa2a7a45fb0ac82e111dbaf11b1697231955db5befa77","source":{"kind":"arxiv","id":"2202.02352","version":3},"attestation_state":"computed","paper":{"title":"Learning Interpretable, High-Performing Policies for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Andrew Silva, Chace Ritchie, Matthew Gombolay, Rohan Paleja, Sugju Choi, Yaru Niu","submitted_at":"2022-02-04T19:20:58Z","abstract_excerpt":"Gradient-based approaches in reinforcement learning (RL) have achieved tremendous success in learning policies for autonomous vehicles. While the performance of these approaches warrants real-world adoption, these policies lack interpretability, limiting deployability in the safety-critical and legally-regulated domain of autonomous driving (AD). AD requires interpretable and verifiable control policies that maintain high performance. We propose Interpretable Continuous Control Trees (ICCTs), a tree-based model that can be optimized via modern, gradient-based, RL approaches to produce high-per"},"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":"2202.02352","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-04T19:20:58Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"1beb875684c92b195667b8055c31d38bbc0995acc4b57afa650cdf5c148beb1b","abstract_canon_sha256":"068e4301bdf4e989ee41f55404f879fa68b52b929395460266575c922d3e2e9b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:35:43.070639Z","signature_b64":"rDSy31rBV94k0/hjibmMvpPVMqKapgp9n73Hd1wKSlYb65ztuR24T35mDjpyjVJJX4rdIeRaZjwSQfS+f00ZBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6de6f665cc29981b02afa2a7a45fb0ac82e111dbaf11b1697231955db5befa77","last_reissued_at":"2026-07-05T06:35:43.070159Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:35:43.070159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Interpretable, High-Performing Policies for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Andrew Silva, Chace Ritchie, Matthew Gombolay, Rohan Paleja, Sugju Choi, Yaru Niu","submitted_at":"2022-02-04T19:20:58Z","abstract_excerpt":"Gradient-based approaches in reinforcement learning (RL) have achieved tremendous success in learning policies for autonomous vehicles. While the performance of these approaches warrants real-world adoption, these policies lack interpretability, limiting deployability in the safety-critical and legally-regulated domain of autonomous driving (AD). AD requires interpretable and verifiable control policies that maintain high performance. We propose Interpretable Continuous Control Trees (ICCTs), a tree-based model that can be optimized via modern, gradient-based, RL approaches to produce high-per"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.02352","kind":"arxiv","version":3},"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/2202.02352/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2202.02352","created_at":"2026-07-05T06:35:43.070209+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.02352v3","created_at":"2026-07-05T06:35:43.070209+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.02352","created_at":"2026-07-05T06:35:43.070209+00:00"},{"alias_kind":"pith_short_12","alias_value":"NXTPMZOMFGMB","created_at":"2026-07-05T06:35:43.070209+00:00"},{"alias_kind":"pith_short_16","alias_value":"NXTPMZOMFGMBWAVP","created_at":"2026-07-05T06:35:43.070209+00:00"},{"alias_kind":"pith_short_8","alias_value":"NXTPMZOM","created_at":"2026-07-05T06:35:43.070209+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/NXTPMZOMFGMBWAVPUKT2IX5QVS","json":"https://pith.science/pith/NXTPMZOMFGMBWAVPUKT2IX5QVS.json","graph_json":"https://pith.science/api/pith-number/NXTPMZOMFGMBWAVPUKT2IX5QVS/graph.json","events_json":"https://pith.science/api/pith-number/NXTPMZOMFGMBWAVPUKT2IX5QVS/events.json","paper":"https://pith.science/paper/NXTPMZOM"},"agent_actions":{"view_html":"https://pith.science/pith/NXTPMZOMFGMBWAVPUKT2IX5QVS","download_json":"https://pith.science/pith/NXTPMZOMFGMBWAVPUKT2IX5QVS.json","view_paper":"https://pith.science/paper/NXTPMZOM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.02352&json=true","fetch_graph":"https://pith.science/api/pith-number/NXTPMZOMFGMBWAVPUKT2IX5QVS/graph.json","fetch_events":"https://pith.science/api/pith-number/NXTPMZOMFGMBWAVPUKT2IX5QVS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NXTPMZOMFGMBWAVPUKT2IX5QVS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NXTPMZOMFGMBWAVPUKT2IX5QVS/action/storage_attestation","attest_author":"https://pith.science/pith/NXTPMZOMFGMBWAVPUKT2IX5QVS/action/author_attestation","sign_citation":"https://pith.science/pith/NXTPMZOMFGMBWAVPUKT2IX5QVS/action/citation_signature","submit_replication":"https://pith.science/pith/NXTPMZOMFGMBWAVPUKT2IX5QVS/action/replication_record"}},"created_at":"2026-07-05T06:35:43.070209+00:00","updated_at":"2026-07-05T06:35:43.070209+00:00"}