{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:OFGEU7XIBYMWSRX5GWEGCZGENH","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":"78dc86b7ad7df83b1722bbd2dec670f0a6f6fc446f9131fa853941cd55cbb425","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-08-19T19:51:53Z","title_canon_sha256":"4c6ffbb4979785e7db2741e801334e0a3faf5d0b62e69edb79d6caf9953ccb75"},"schema_version":"1.0","source":{"id":"2408.10381","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.10381","created_at":"2026-07-05T08:57:11Z"},{"alias_kind":"arxiv_version","alias_value":"2408.10381v1","created_at":"2026-07-05T08:57:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.10381","created_at":"2026-07-05T08:57:11Z"},{"alias_kind":"pith_short_12","alias_value":"OFGEU7XIBYMW","created_at":"2026-07-05T08:57:11Z"},{"alias_kind":"pith_short_16","alias_value":"OFGEU7XIBYMWSRX5","created_at":"2026-07-05T08:57:11Z"},{"alias_kind":"pith_short_8","alias_value":"OFGEU7XI","created_at":"2026-07-05T08:57:11Z"}],"graph_snapshots":[{"event_id":"sha256:9238ef62cda260c8a020c4cb2d3254c73dea157e8f2c04f76acf01ac74c62a8a","target":"graph","created_at":"2026-07-05T08:57:11Z","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/2408.10381/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we study reinforcement learning in Markov Decision Processes with Probabilistic Reward Machines (PRMs), a form of non-Markovian reward commonly found in robotics tasks. We design an algorithm for PRMs that achieves a regret bound of $\\widetilde{O}(\\sqrt{HOAT} + H^2O^2A^{3/2} + H\\sqrt{T})$, where $H$ is the time horizon, $O$ is the number of observations, $A$ is the number of actions, and $T$ is the number of time-steps. This result improves over the best-known bound, $\\widetilde{O}(H\\sqrt{OAT})$ of \\citet{pmlr-v206-bourel23a} for MDPs with Deterministic Reward Machines (DRMs), a","authors_text":"Xiaofeng Lin, Xuezhou Zhang","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-08-19T19:51:53Z","title":"Efficient Reinforcement Learning in Probabilistic Reward Machines"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.10381","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":"sha256:385adb370f0f43be0b6b389d140a106c22caab30ac2f433a3ca6008a1a53e05d","target":"record","created_at":"2026-07-05T08:57:11Z","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":"78dc86b7ad7df83b1722bbd2dec670f0a6f6fc446f9131fa853941cd55cbb425","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-08-19T19:51:53Z","title_canon_sha256":"4c6ffbb4979785e7db2741e801334e0a3faf5d0b62e69edb79d6caf9953ccb75"},"schema_version":"1.0","source":{"id":"2408.10381","kind":"arxiv","version":1}},"canonical_sha256":"714c4a7ee80e196946fd35886164c469fbaa23d7847e36f9163d8e3a1eb77e5d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"714c4a7ee80e196946fd35886164c469fbaa23d7847e36f9163d8e3a1eb77e5d","first_computed_at":"2026-07-05T08:57:11.078255Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:57:11.078255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7t7slbQerhkYYxFtgE6XWfuQ5DiWqoutd7JFx94FoiYgo6WQiwQun3NCHcGmpfx0Cngofyofin3gXIq4qu/ZAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:57:11.078639Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.10381","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:385adb370f0f43be0b6b389d140a106c22caab30ac2f433a3ca6008a1a53e05d","sha256:9238ef62cda260c8a020c4cb2d3254c73dea157e8f2c04f76acf01ac74c62a8a"],"state_sha256":"026aa175ea7b15dc8fa17dfceac26116e21af36bc02ff95367b13ddedbce1051"}