{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:F4I5ZO42CJY2UEJZELETWG5FZT","short_pith_number":"pith:F4I5ZO42","canonical_record":{"source":{"id":"2201.12609","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-01-29T15:46:36Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"6ced108569b2c2652121286a9a9aee9d38278940c07b39a6a321ba5dca8b98de","abstract_canon_sha256":"0e516aa44ed2887aa4ac07dce9c25f402a66482a99c5b979f65c726522d13d08"},"schema_version":"1.0"},"canonical_sha256":"2f11dcbb9a1271aa113922c93b1ba5ccc29cebe57ce46ea8f83520b50f265ab0","source":{"kind":"arxiv","id":"2201.12609","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.12609","created_at":"2026-07-05T03:52:38Z"},{"alias_kind":"arxiv_version","alias_value":"2201.12609v1","created_at":"2026-07-05T03:52:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.12609","created_at":"2026-07-05T03:52:38Z"},{"alias_kind":"pith_short_12","alias_value":"F4I5ZO42CJY2","created_at":"2026-07-05T03:52:38Z"},{"alias_kind":"pith_short_16","alias_value":"F4I5ZO42CJY2UEJZ","created_at":"2026-07-05T03:52:38Z"},{"alias_kind":"pith_short_8","alias_value":"F4I5ZO42","created_at":"2026-07-05T03:52:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:F4I5ZO42CJY2UEJZELETWG5FZT","target":"record","payload":{"canonical_record":{"source":{"id":"2201.12609","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-01-29T15:46:36Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"6ced108569b2c2652121286a9a9aee9d38278940c07b39a6a321ba5dca8b98de","abstract_canon_sha256":"0e516aa44ed2887aa4ac07dce9c25f402a66482a99c5b979f65c726522d13d08"},"schema_version":"1.0"},"canonical_sha256":"2f11dcbb9a1271aa113922c93b1ba5ccc29cebe57ce46ea8f83520b50f265ab0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:52:38.224248Z","signature_b64":"1Ob5Zcr6AaXKDIfKlS987jcS/Hr7bNhzI46QqrvJBVvJLuiq9WiFcPkNefWdg4EWPmr0hc4PiX4kTb8HhZawBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f11dcbb9a1271aa113922c93b1ba5ccc29cebe57ce46ea8f83520b50f265ab0","last_reissued_at":"2026-07-05T03:52:38.223729Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:52:38.223729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2201.12609","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:52:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z3SiQ4KpX852zjT3m1brGNarhBzJ2jMfTxcjWJVRIv7xVOXFa2N7Dr0CxiHnlpGSE1A+NDvT7e2ioJCaBDlIDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T20:44:13.374732Z"},"content_sha256":"690ca87681e18364d1c8350ed09203cf0b527a9eb1e50ab58ef54013f94694d4","schema_version":"1.0","event_id":"sha256:690ca87681e18364d1c8350ed09203cf0b527a9eb1e50ab58ef54013f94694d4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:F4I5ZO42CJY2UEJZELETWG5FZT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ApolloRL: a Reinforcement Learning Platform for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.RO","authors_text":"Dingfeng Guo, Fei Gao, Jiaqi Guo, Jie Zhou, Jin Li, Peng Geng, Xiao Wei, Xu Liu, Yabo Su, Yuan Liu","submitted_at":"2022-01-29T15:46:36Z","abstract_excerpt":"We introduce ApolloRL, an open platform for research in reinforcement learning for autonomous driving. The platform provides a complete closed-loop pipeline with training, simulation, and evaluation components. It comes with 300 hours of real-world data in driving scenarios and popular baselines such as Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) agents. We elaborate in this paper on the architecture and the environment defined in the platform. In addition, we discuss the performance of the baseline agents in the ApolloRL environment."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.12609","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2201.12609/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":"2026-07-05T03:52:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0KpDL6UZlHFdnVScbA7B9/9AtbxuCCbEBLb23F9fWDmVrDE/Y7dxr5kT7MjOi3cReqi57ocODyX15wVmM8whCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T20:44:13.375493Z"},"content_sha256":"edb8f0cb77a4f436b408c306a64916c8c97bca6e7eb8d249988627f72d21db23","schema_version":"1.0","event_id":"sha256:edb8f0cb77a4f436b408c306a64916c8c97bca6e7eb8d249988627f72d21db23"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F4I5ZO42CJY2UEJZELETWG5FZT/bundle.json","state_url":"https://pith.science/pith/F4I5ZO42CJY2UEJZELETWG5FZT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F4I5ZO42CJY2UEJZELETWG5FZT/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":"2026-08-13T20:44:13Z","links":{"resolver":"https://pith.science/pith/F4I5ZO42CJY2UEJZELETWG5FZT","bundle":"https://pith.science/pith/F4I5ZO42CJY2UEJZELETWG5FZT/bundle.json","state":"https://pith.science/pith/F4I5ZO42CJY2UEJZELETWG5FZT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F4I5ZO42CJY2UEJZELETWG5FZT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:F4I5ZO42CJY2UEJZELETWG5FZT","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":"0e516aa44ed2887aa4ac07dce9c25f402a66482a99c5b979f65c726522d13d08","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-01-29T15:46:36Z","title_canon_sha256":"6ced108569b2c2652121286a9a9aee9d38278940c07b39a6a321ba5dca8b98de"},"schema_version":"1.0","source":{"id":"2201.12609","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.12609","created_at":"2026-07-05T03:52:38Z"},{"alias_kind":"arxiv_version","alias_value":"2201.12609v1","created_at":"2026-07-05T03:52:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.12609","created_at":"2026-07-05T03:52:38Z"},{"alias_kind":"pith_short_12","alias_value":"F4I5ZO42CJY2","created_at":"2026-07-05T03:52:38Z"},{"alias_kind":"pith_short_16","alias_value":"F4I5ZO42CJY2UEJZ","created_at":"2026-07-05T03:52:38Z"},{"alias_kind":"pith_short_8","alias_value":"F4I5ZO42","created_at":"2026-07-05T03:52:38Z"}],"graph_snapshots":[{"event_id":"sha256:edb8f0cb77a4f436b408c306a64916c8c97bca6e7eb8d249988627f72d21db23","target":"graph","created_at":"2026-07-05T03:52:38Z","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/2201.12609/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce ApolloRL, an open platform for research in reinforcement learning for autonomous driving. The platform provides a complete closed-loop pipeline with training, simulation, and evaluation components. It comes with 300 hours of real-world data in driving scenarios and popular baselines such as Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) agents. We elaborate in this paper on the architecture and the environment defined in the platform. In addition, we discuss the performance of the baseline agents in the ApolloRL environment.","authors_text":"Dingfeng Guo, Fei Gao, Jiaqi Guo, Jie Zhou, Jin Li, Peng Geng, Xiao Wei, Xu Liu, Yabo Su, Yuan Liu","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-01-29T15:46:36Z","title":"ApolloRL: a Reinforcement Learning Platform for Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.12609","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":"sha256:690ca87681e18364d1c8350ed09203cf0b527a9eb1e50ab58ef54013f94694d4","target":"record","created_at":"2026-07-05T03:52:38Z","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":"0e516aa44ed2887aa4ac07dce9c25f402a66482a99c5b979f65c726522d13d08","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-01-29T15:46:36Z","title_canon_sha256":"6ced108569b2c2652121286a9a9aee9d38278940c07b39a6a321ba5dca8b98de"},"schema_version":"1.0","source":{"id":"2201.12609","kind":"arxiv","version":1}},"canonical_sha256":"2f11dcbb9a1271aa113922c93b1ba5ccc29cebe57ce46ea8f83520b50f265ab0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f11dcbb9a1271aa113922c93b1ba5ccc29cebe57ce46ea8f83520b50f265ab0","first_computed_at":"2026-07-05T03:52:38.223729Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:52:38.223729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1Ob5Zcr6AaXKDIfKlS987jcS/Hr7bNhzI46QqrvJBVvJLuiq9WiFcPkNefWdg4EWPmr0hc4PiX4kTb8HhZawBA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:52:38.224248Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.12609","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:690ca87681e18364d1c8350ed09203cf0b527a9eb1e50ab58ef54013f94694d4","sha256:edb8f0cb77a4f436b408c306a64916c8c97bca6e7eb8d249988627f72d21db23"],"state_sha256":"e808e4ad6dbf95d6fb000abf77e41f501a7e4459f055fb8a51176b86da3e6d0a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e/t4WnN9fSske/iWiBHw+Cg9W08Mb5n6SkJ5Orulx6NTqppvf77ZQdKg0O7DnIMKyQ3uIUVxAhSHz/EDnirmAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T20:44:13.391560Z","bundle_sha256":"2c87945f1587654fe97e49d8dab9cf5ed913c4b89a44937fea2c3fca1b3feaa3"}}