{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:ETXISJABNBG7AJZH6IG2NLSJQ2","short_pith_number":"pith:ETXISJAB","canonical_record":{"source":{"id":"1710.10116","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-10-27T13:10:26Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"fb3aa150f41ce7cdf332541423c930c5f581822e4b195f72be1eef0fb3c37710","abstract_canon_sha256":"9dd4e71972a65399a3e6cc9bea833c1dbce50c3e8516c0d80dcd9c4c5ed970a2"},"schema_version":"1.0"},"canonical_sha256":"24ee892401684df02727f20da6ae4986983ed74beb34ecc894c9d2968f19741d","source":{"kind":"arxiv","id":"1710.10116","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10116","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10116v1","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10116","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"pith_short_12","alias_value":"ETXISJABNBG7","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"ETXISJABNBG7AJZH","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"ETXISJAB","created_at":"2026-05-18T12:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:ETXISJABNBG7AJZH6IG2NLSJQ2","target":"record","payload":{"canonical_record":{"source":{"id":"1710.10116","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-10-27T13:10:26Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"fb3aa150f41ce7cdf332541423c930c5f581822e4b195f72be1eef0fb3c37710","abstract_canon_sha256":"9dd4e71972a65399a3e6cc9bea833c1dbce50c3e8516c0d80dcd9c4c5ed970a2"},"schema_version":"1.0"},"canonical_sha256":"24ee892401684df02727f20da6ae4986983ed74beb34ecc894c9d2968f19741d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:54.726192Z","signature_b64":"JaQm/d9uPG+DdJoNUtGeTubz58dLrzQPu0x0TwwANcSSDb39Yts4pM/u0yeqAsskq0PYwBzULOOX7bv/cUBYBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24ee892401684df02727f20da6ae4986983ed74beb34ecc894c9d2968f19741d","last_reissued_at":"2026-05-18T00:31:54.725585Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:54.725585Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.10116","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":"2026-05-18T00:31:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q0AgMPSXce/qKLmkGO+DrRlWesgKA0cW2+UXVaKL4ffO2aRAWsYK0jzZGfjshUwPImTUtSSufKGUJGLhlIjgAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T02:33:49.255659Z"},"content_sha256":"0cd0c9deb9ad4e34a61e28c57ff7fa7d7e880d95d9bae54aea9eb78859b9f536","schema_version":"1.0","event_id":"sha256:0cd0c9deb9ad4e34a61e28c57ff7fa7d7e880d95d9bae54aea9eb78859b9f536"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:ETXISJABNBG7AJZH6IG2NLSJQ2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Inverse Reinforcement Learning Under Noisy Observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Prashant Doshi, Shervin Shahryari","submitted_at":"2017-10-27T13:10:26Z","abstract_excerpt":"We consider the problem of performing inverse reinforcement learning when the trajectory of the expert is not perfectly observed by the learner. Instead, a noisy continuous-time observation of the trajectory is provided to the learner. This problem exhibits wide-ranging applications and the specific application we consider here is the scenario in which the learner seeks to penetrate a perimeter patrolled by a robot. The learner's field of view is limited due to which it cannot observe the patroller's complete trajectory. Instead, we allow the learner to listen to the expert's movement sound, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10116","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":"2026-05-18T00:31:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GvndGa2aroM/oky6a3QjAJr3x99ZIAL3y+1wY/1iKw1xwvI9yq9tzvJj1TBf+PO8N/h4Cud/fdbdv4XsGAQNCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T02:33:49.256008Z"},"content_sha256":"bf9fe142d3c1f9c063fd4341d577d4f90a37fd0c903ec26f6684e88a4856842e","schema_version":"1.0","event_id":"sha256:bf9fe142d3c1f9c063fd4341d577d4f90a37fd0c903ec26f6684e88a4856842e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ETXISJABNBG7AJZH6IG2NLSJQ2/bundle.json","state_url":"https://pith.science/pith/ETXISJABNBG7AJZH6IG2NLSJQ2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ETXISJABNBG7AJZH6IG2NLSJQ2/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":"2026-06-02T02:33:49Z","links":{"resolver":"https://pith.science/pith/ETXISJABNBG7AJZH6IG2NLSJQ2","bundle":"https://pith.science/pith/ETXISJABNBG7AJZH6IG2NLSJQ2/bundle.json","state":"https://pith.science/pith/ETXISJABNBG7AJZH6IG2NLSJQ2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ETXISJABNBG7AJZH6IG2NLSJQ2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ETXISJABNBG7AJZH6IG2NLSJQ2","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":"9dd4e71972a65399a3e6cc9bea833c1dbce50c3e8516c0d80dcd9c4c5ed970a2","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-10-27T13:10:26Z","title_canon_sha256":"fb3aa150f41ce7cdf332541423c930c5f581822e4b195f72be1eef0fb3c37710"},"schema_version":"1.0","source":{"id":"1710.10116","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10116","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10116v1","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10116","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"pith_short_12","alias_value":"ETXISJABNBG7","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"ETXISJABNBG7AJZH","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"ETXISJAB","created_at":"2026-05-18T12:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:bf9fe142d3c1f9c063fd4341d577d4f90a37fd0c903ec26f6684e88a4856842e","target":"graph","created_at":"2026-05-18T00:31:54Z","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":"We consider the problem of performing inverse reinforcement learning when the trajectory of the expert is not perfectly observed by the learner. Instead, a noisy continuous-time observation of the trajectory is provided to the learner. This problem exhibits wide-ranging applications and the specific application we consider here is the scenario in which the learner seeks to penetrate a perimeter patrolled by a robot. The learner's field of view is limited due to which it cannot observe the patroller's complete trajectory. Instead, we allow the learner to listen to the expert's movement sound, w","authors_text":"Prashant Doshi, Shervin Shahryari","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-10-27T13:10:26Z","title":"Inverse Reinforcement Learning Under Noisy Observations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10116","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":"sha256:0cd0c9deb9ad4e34a61e28c57ff7fa7d7e880d95d9bae54aea9eb78859b9f536","target":"record","created_at":"2026-05-18T00:31:54Z","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":"9dd4e71972a65399a3e6cc9bea833c1dbce50c3e8516c0d80dcd9c4c5ed970a2","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-10-27T13:10:26Z","title_canon_sha256":"fb3aa150f41ce7cdf332541423c930c5f581822e4b195f72be1eef0fb3c37710"},"schema_version":"1.0","source":{"id":"1710.10116","kind":"arxiv","version":1}},"canonical_sha256":"24ee892401684df02727f20da6ae4986983ed74beb34ecc894c9d2968f19741d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24ee892401684df02727f20da6ae4986983ed74beb34ecc894c9d2968f19741d","first_computed_at":"2026-05-18T00:31:54.725585Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:54.725585Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JaQm/d9uPG+DdJoNUtGeTubz58dLrzQPu0x0TwwANcSSDb39Yts4pM/u0yeqAsskq0PYwBzULOOX7bv/cUBYBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:54.726192Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.10116","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0cd0c9deb9ad4e34a61e28c57ff7fa7d7e880d95d9bae54aea9eb78859b9f536","sha256:bf9fe142d3c1f9c063fd4341d577d4f90a37fd0c903ec26f6684e88a4856842e"],"state_sha256":"6f2de5168ab33d6017992125c4665d4faa99755973c849752c18b0c1ac660b1e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pwRDvod41pI1N/oVp+WwcMc0rUZwhn7Jnp8S0v6r6B7c+HsfYBriHqncG8edVSdDNFbU09oiuVW5WS40SJ5JBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T02:33:49.258009Z","bundle_sha256":"7a4245120d2506dbaa39cf2645e3cabf949b221e1cb1a1ed762beda75698ceb8"}}