{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:JF5WHBKNA4TGB4HVJUVO66OQRN","short_pith_number":"pith:JF5WHBKN","canonical_record":{"source":{"id":"1412.3038","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2014-12-09T18:03:57Z","cross_cats_sorted":[],"title_canon_sha256":"f18a34f81e8a7575da9405834361e806df643ce31fec0c848c16ce1a3026f5c3","abstract_canon_sha256":"943556c592b7512edfe8b9423a6e546ce98d0fc42fb0485f8e5aeeaca008bbee"},"schema_version":"1.0"},"canonical_sha256":"497b63854d072660f0f54d2aef79d08b49f6e41a275c45f080068d1d4aa1ef89","source":{"kind":"arxiv","id":"1412.3038","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.3038","created_at":"2026-05-18T02:31:45Z"},{"alias_kind":"arxiv_version","alias_value":"1412.3038v1","created_at":"2026-05-18T02:31:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.3038","created_at":"2026-05-18T02:31:45Z"},{"alias_kind":"pith_short_12","alias_value":"JF5WHBKNA4TG","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_16","alias_value":"JF5WHBKNA4TGB4HV","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_8","alias_value":"JF5WHBKN","created_at":"2026-05-18T12:28:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:JF5WHBKNA4TGB4HVJUVO66OQRN","target":"record","payload":{"canonical_record":{"source":{"id":"1412.3038","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2014-12-09T18:03:57Z","cross_cats_sorted":[],"title_canon_sha256":"f18a34f81e8a7575da9405834361e806df643ce31fec0c848c16ce1a3026f5c3","abstract_canon_sha256":"943556c592b7512edfe8b9423a6e546ce98d0fc42fb0485f8e5aeeaca008bbee"},"schema_version":"1.0"},"canonical_sha256":"497b63854d072660f0f54d2aef79d08b49f6e41a275c45f080068d1d4aa1ef89","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:31:45.127301Z","signature_b64":"t3L+ScfYO+pb8PYRGU72mnVoPYHXLyooEIOFFNa/vDkYvojyKFbDqM31OMkyYz2aa2jI2BC7Onz7RMML8ZnEBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"497b63854d072660f0f54d2aef79d08b49f6e41a275c45f080068d1d4aa1ef89","last_reissued_at":"2026-05-18T02:31:45.126637Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:31:45.126637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1412.3038","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":"2026-05-18T02:31:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a1ifdEdrSYWfrvW7nOAp6MLisO8BoP42znPcWPgaq61bXN4Se26532hWyqEgC0i/05mwcVH+RzDoqavWv5fDAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T16:18:58.046037Z"},"content_sha256":"ac6a43b725ce2adeb9c94bc18dba4c7e6010649a16a4897c8f752b40c87e47be","schema_version":"1.0","event_id":"sha256:ac6a43b725ce2adeb9c94bc18dba4c7e6010649a16a4897c8f752b40c87e47be"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:JF5WHBKNA4TGB4HVJUVO66OQRN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Model-based Path Integral Stochastic Control: A Bayesian Nonparametric Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SY","authors_text":"Evangelos A. Theodorou, Michail Kontitsis, Yunpeng Pan","submitted_at":"2014-12-09T18:03:57Z","abstract_excerpt":"Over the last few years, sampling-based stochastic optimal control (SOC) frameworks have shown impressive performances in reinforcement learning (RL) with applications in robotics. However, such approaches require a large amount of samples from many interactions with the physical systems. To improve learning efficiency, we present a novel model-based and data-driven SOC framework based on path integral formulation and Gaussian processes (GPs). The proposed approach learns explicit and time-varying optimal controls autonomously from limited sampled data. Based on this framework, we propose an i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.3038","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":""},"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":"2026-05-18T02:31:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sRaYvLoa1aHcmhtHebIa9lLtQRIucugkrBvO3LMAUWk1VqehakFxt9pusa3cMBbJe+VaO9T7amROyANxeZ16AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T16:18:58.046745Z"},"content_sha256":"7441193c33557820bfbaa08544c1d095bd63db3f0a2744256243912534a93b37","schema_version":"1.0","event_id":"sha256:7441193c33557820bfbaa08544c1d095bd63db3f0a2744256243912534a93b37"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JF5WHBKNA4TGB4HVJUVO66OQRN/bundle.json","state_url":"https://pith.science/pith/JF5WHBKNA4TGB4HVJUVO66OQRN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JF5WHBKNA4TGB4HVJUVO66OQRN/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":"2026-05-30T16:18:58Z","links":{"resolver":"https://pith.science/pith/JF5WHBKNA4TGB4HVJUVO66OQRN","bundle":"https://pith.science/pith/JF5WHBKNA4TGB4HVJUVO66OQRN/bundle.json","state":"https://pith.science/pith/JF5WHBKNA4TGB4HVJUVO66OQRN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JF5WHBKNA4TGB4HVJUVO66OQRN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:JF5WHBKNA4TGB4HVJUVO66OQRN","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":"943556c592b7512edfe8b9423a6e546ce98d0fc42fb0485f8e5aeeaca008bbee","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2014-12-09T18:03:57Z","title_canon_sha256":"f18a34f81e8a7575da9405834361e806df643ce31fec0c848c16ce1a3026f5c3"},"schema_version":"1.0","source":{"id":"1412.3038","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.3038","created_at":"2026-05-18T02:31:45Z"},{"alias_kind":"arxiv_version","alias_value":"1412.3038v1","created_at":"2026-05-18T02:31:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.3038","created_at":"2026-05-18T02:31:45Z"},{"alias_kind":"pith_short_12","alias_value":"JF5WHBKNA4TG","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_16","alias_value":"JF5WHBKNA4TGB4HV","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_8","alias_value":"JF5WHBKN","created_at":"2026-05-18T12:28:33Z"}],"graph_snapshots":[{"event_id":"sha256:7441193c33557820bfbaa08544c1d095bd63db3f0a2744256243912534a93b37","target":"graph","created_at":"2026-05-18T02:31:45Z","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"},"paper":{"abstract_excerpt":"Over the last few years, sampling-based stochastic optimal control (SOC) frameworks have shown impressive performances in reinforcement learning (RL) with applications in robotics. However, such approaches require a large amount of samples from many interactions with the physical systems. To improve learning efficiency, we present a novel model-based and data-driven SOC framework based on path integral formulation and Gaussian processes (GPs). The proposed approach learns explicit and time-varying optimal controls autonomously from limited sampled data. Based on this framework, we propose an i","authors_text":"Evangelos A. Theodorou, Michail Kontitsis, Yunpeng Pan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2014-12-09T18:03:57Z","title":"Model-based Path Integral Stochastic Control: A Bayesian Nonparametric Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.3038","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:ac6a43b725ce2adeb9c94bc18dba4c7e6010649a16a4897c8f752b40c87e47be","target":"record","created_at":"2026-05-18T02:31:45Z","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":"943556c592b7512edfe8b9423a6e546ce98d0fc42fb0485f8e5aeeaca008bbee","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2014-12-09T18:03:57Z","title_canon_sha256":"f18a34f81e8a7575da9405834361e806df643ce31fec0c848c16ce1a3026f5c3"},"schema_version":"1.0","source":{"id":"1412.3038","kind":"arxiv","version":1}},"canonical_sha256":"497b63854d072660f0f54d2aef79d08b49f6e41a275c45f080068d1d4aa1ef89","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"497b63854d072660f0f54d2aef79d08b49f6e41a275c45f080068d1d4aa1ef89","first_computed_at":"2026-05-18T02:31:45.126637Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:31:45.126637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"t3L+ScfYO+pb8PYRGU72mnVoPYHXLyooEIOFFNa/vDkYvojyKFbDqM31OMkyYz2aa2jI2BC7Onz7RMML8ZnEBA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:31:45.127301Z","signed_message":"canonical_sha256_bytes"},"source_id":"1412.3038","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ac6a43b725ce2adeb9c94bc18dba4c7e6010649a16a4897c8f752b40c87e47be","sha256:7441193c33557820bfbaa08544c1d095bd63db3f0a2744256243912534a93b37"],"state_sha256":"db042d148c26422de7aa8a9cf87bea92b5ba05d6238544001e2640c23dab033f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rP+/tEyfZ8jZVYAt4Xb/lYovPmj8zWyBieec2M9vai2LxUoclOLSZGC0phRbfxYLJ1JE34tTUnq90Niseg9lBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T16:18:58.050148Z","bundle_sha256":"696602d39cf36a778619cf40611f1e12e446d2d06a4ddd66c639237f7f29d9b8"}}