{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:GNISDVH6CSTB2OOPGEHY5PGAVG","short_pith_number":"pith:GNISDVH6","canonical_record":{"source":{"id":"2006.10742","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-18T17:59:35Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"5e5ff642737d2bdf07fa74bc0977ccc9c561b3037b03e381f6dc044c89a81aa2","abstract_canon_sha256":"a5ac723821cb6a676c5c71cebe424405b557d833465d3d68e61423a2e655c118"},"schema_version":"1.0"},"canonical_sha256":"335121d4fe14a61d39cf310f8ebcc0a991f3a778ee441946d0c5a904a0d8b906","source":{"kind":"arxiv","id":"2006.10742","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.10742","created_at":"2026-07-05T02:29:53Z"},{"alias_kind":"arxiv_version","alias_value":"2006.10742v2","created_at":"2026-07-05T02:29:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.10742","created_at":"2026-07-05T02:29:53Z"},{"alias_kind":"pith_short_12","alias_value":"GNISDVH6CSTB","created_at":"2026-07-05T02:29:53Z"},{"alias_kind":"pith_short_16","alias_value":"GNISDVH6CSTB2OOP","created_at":"2026-07-05T02:29:53Z"},{"alias_kind":"pith_short_8","alias_value":"GNISDVH6","created_at":"2026-07-05T02:29:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:GNISDVH6CSTB2OOPGEHY5PGAVG","target":"record","payload":{"canonical_record":{"source":{"id":"2006.10742","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-18T17:59:35Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"5e5ff642737d2bdf07fa74bc0977ccc9c561b3037b03e381f6dc044c89a81aa2","abstract_canon_sha256":"a5ac723821cb6a676c5c71cebe424405b557d833465d3d68e61423a2e655c118"},"schema_version":"1.0"},"canonical_sha256":"335121d4fe14a61d39cf310f8ebcc0a991f3a778ee441946d0c5a904a0d8b906","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:29:53.895710Z","signature_b64":"P5eIwMannzmltJ4bUBuUCRNu51QIj3evzN9GaeQLpD7aYlwvoGqstjsf4pCNCj6mLczuE9c8c9CSXDDwBOC6AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"335121d4fe14a61d39cf310f8ebcc0a991f3a778ee441946d0c5a904a0d8b906","last_reissued_at":"2026-07-05T02:29:53.895227Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:29:53.895227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.10742","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:29:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+EsgFr6OsBCVTdiwKDQxrvgZ1lUmsBlOEI/6gx2AwphxUpCTpNgSBanx7y5DA23d7GdsBgLTkZYKiqoOe+IXDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T09:39:56.655575Z"},"content_sha256":"9b7d12e8b9a19bf073114bef18d90235300fae2bdaf309c54a99c1e8d1674eef","schema_version":"1.0","event_id":"sha256:9b7d12e8b9a19bf073114bef18d90235300fae2bdaf309c54a99c1e8d1674eef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:GNISDVH6CSTB2OOPGEHY5PGAVG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Invariant Representations for Reinforcement Learning without Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Amy Zhang, Roberto Calandra, Rowan McAllister, Sergey Levine, Yarin Gal","submitted_at":"2020-06-18T17:59:35Z","abstract_excerpt":"We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstruction. Our goal is to learn representations that both provide for effective downstream control and invariance to task-irrelevant details. Bisimulation metrics quantify behavioral similarity between states in continuous MDPs, which we propose using to learn robust latent representations which encode only the task-relevant information from observations. Our method trains encoders such that distances in latent space equal b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.10742","kind":"arxiv","version":2},"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/2006.10742/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":"2026-07-05T02:29:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VElj1OgBm3UPPSPbTSXjnEOFatG+vDr/zzwJnyk5fgws27UKeMc9l1Yyo4BmFJYYftyZJS3Gskz3p3hEFSVbBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T09:39:56.656084Z"},"content_sha256":"944a34ea8fd5e1225b4b830012a1e87ed43a3e23c01f82ee793702465c7a8938","schema_version":"1.0","event_id":"sha256:944a34ea8fd5e1225b4b830012a1e87ed43a3e23c01f82ee793702465c7a8938"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GNISDVH6CSTB2OOPGEHY5PGAVG/bundle.json","state_url":"https://pith.science/pith/GNISDVH6CSTB2OOPGEHY5PGAVG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GNISDVH6CSTB2OOPGEHY5PGAVG/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":"2026-08-17T09:39:56Z","links":{"resolver":"https://pith.science/pith/GNISDVH6CSTB2OOPGEHY5PGAVG","bundle":"https://pith.science/pith/GNISDVH6CSTB2OOPGEHY5PGAVG/bundle.json","state":"https://pith.science/pith/GNISDVH6CSTB2OOPGEHY5PGAVG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GNISDVH6CSTB2OOPGEHY5PGAVG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:GNISDVH6CSTB2OOPGEHY5PGAVG","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":"a5ac723821cb6a676c5c71cebe424405b557d833465d3d68e61423a2e655c118","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-18T17:59:35Z","title_canon_sha256":"5e5ff642737d2bdf07fa74bc0977ccc9c561b3037b03e381f6dc044c89a81aa2"},"schema_version":"1.0","source":{"id":"2006.10742","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.10742","created_at":"2026-07-05T02:29:53Z"},{"alias_kind":"arxiv_version","alias_value":"2006.10742v2","created_at":"2026-07-05T02:29:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.10742","created_at":"2026-07-05T02:29:53Z"},{"alias_kind":"pith_short_12","alias_value":"GNISDVH6CSTB","created_at":"2026-07-05T02:29:53Z"},{"alias_kind":"pith_short_16","alias_value":"GNISDVH6CSTB2OOP","created_at":"2026-07-05T02:29:53Z"},{"alias_kind":"pith_short_8","alias_value":"GNISDVH6","created_at":"2026-07-05T02:29:53Z"}],"graph_snapshots":[{"event_id":"sha256:944a34ea8fd5e1225b4b830012a1e87ed43a3e23c01f82ee793702465c7a8938","target":"graph","created_at":"2026-07-05T02:29:53Z","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/2006.10742/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstruction. Our goal is to learn representations that both provide for effective downstream control and invariance to task-irrelevant details. Bisimulation metrics quantify behavioral similarity between states in continuous MDPs, which we propose using to learn robust latent representations which encode only the task-relevant information from observations. Our method trains encoders such that distances in latent space equal b","authors_text":"Amy Zhang, Roberto Calandra, Rowan McAllister, Sergey Levine, Yarin Gal","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-18T17:59:35Z","title":"Learning Invariant Representations for Reinforcement Learning without Reconstruction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.10742","kind":"arxiv","version":2},"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":"sha256:9b7d12e8b9a19bf073114bef18d90235300fae2bdaf309c54a99c1e8d1674eef","target":"record","created_at":"2026-07-05T02:29:53Z","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":"a5ac723821cb6a676c5c71cebe424405b557d833465d3d68e61423a2e655c118","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-18T17:59:35Z","title_canon_sha256":"5e5ff642737d2bdf07fa74bc0977ccc9c561b3037b03e381f6dc044c89a81aa2"},"schema_version":"1.0","source":{"id":"2006.10742","kind":"arxiv","version":2}},"canonical_sha256":"335121d4fe14a61d39cf310f8ebcc0a991f3a778ee441946d0c5a904a0d8b906","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"335121d4fe14a61d39cf310f8ebcc0a991f3a778ee441946d0c5a904a0d8b906","first_computed_at":"2026-07-05T02:29:53.895227Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:29:53.895227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"P5eIwMannzmltJ4bUBuUCRNu51QIj3evzN9GaeQLpD7aYlwvoGqstjsf4pCNCj6mLczuE9c8c9CSXDDwBOC6AA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:29:53.895710Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.10742","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9b7d12e8b9a19bf073114bef18d90235300fae2bdaf309c54a99c1e8d1674eef","sha256:944a34ea8fd5e1225b4b830012a1e87ed43a3e23c01f82ee793702465c7a8938"],"state_sha256":"ec32832b46e32820941d0b56fef82d15340fdb36e9965afb89ea8b6edb4dda6d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tIcWIJFtPhaUFxdhaQdKkHEobv9Fsgw4K3t5JnkwsUNOOm4mi3AADjFndzt1PbtR7gW2ed1gDR0Oj7heADB9Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T09:39:56.661368Z","bundle_sha256":"7ad8aeda5210a1f839bb454cf932c892b4ee20a9e6667cfa208b1007980426e9"}}