{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:3ZWCR3BZI2ULGKUNTB2WXUYWI6","short_pith_number":"pith:3ZWCR3BZ","canonical_record":{"source":{"id":"1906.08226","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-19T17:16:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c8b701c496f09490722a2db32a0d62181cb191d14fab05aae592cec06876e452","abstract_canon_sha256":"47b4bd7bf1509f75bee06e0fe1e857cd6cd16a5d0b21e08e2b931fd01ceacdc0"},"schema_version":"1.0"},"canonical_sha256":"de6c28ec3946a8b32a8d98756bd31647bb2fa25186e459ef220b25b8c5f462ef","source":{"kind":"arxiv","id":"1906.08226","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.08226","created_at":"2026-07-05T01:49:34Z"},{"alias_kind":"arxiv_version","alias_value":"1906.08226v6","created_at":"2026-07-05T01:49:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.08226","created_at":"2026-07-05T01:49:34Z"},{"alias_kind":"pith_short_12","alias_value":"3ZWCR3BZI2UL","created_at":"2026-07-05T01:49:34Z"},{"alias_kind":"pith_short_16","alias_value":"3ZWCR3BZI2ULGKUN","created_at":"2026-07-05T01:49:34Z"},{"alias_kind":"pith_short_8","alias_value":"3ZWCR3BZ","created_at":"2026-07-05T01:49:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:3ZWCR3BZI2ULGKUNTB2WXUYWI6","target":"record","payload":{"canonical_record":{"source":{"id":"1906.08226","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-19T17:16:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c8b701c496f09490722a2db32a0d62181cb191d14fab05aae592cec06876e452","abstract_canon_sha256":"47b4bd7bf1509f75bee06e0fe1e857cd6cd16a5d0b21e08e2b931fd01ceacdc0"},"schema_version":"1.0"},"canonical_sha256":"de6c28ec3946a8b32a8d98756bd31647bb2fa25186e459ef220b25b8c5f462ef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:49:34.888098Z","signature_b64":"4ZIWgKd+7n4VJRpJyIsqtsFgPzhjkswmmNUgRBdhZelW0p007ck7hQNWhO0jEMi83gERk+kY0HPKLahrF4IqBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de6c28ec3946a8b32a8d98756bd31647bb2fa25186e459ef220b25b8c5f462ef","last_reissued_at":"2026-07-05T01:49:34.887559Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:49:34.887559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.08226","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:49:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N54pZ/n/3PIETOh+m0DtLrVaLUdh4AiriGOm/eSspUaOxMtjuMoASlsc84XY/BN1wjOpov/G3LCyTvspxIX1Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:13:04.510109Z"},"content_sha256":"a2be8cf5b182519f85621ecee71c9b516b948a01cd15a565e01e29a07f4cba3f","schema_version":"1.0","event_id":"sha256:a2be8cf5b182519f85621ecee71c9b516b948a01cd15a565e01e29a07f4cba3f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:3ZWCR3BZI2ULGKUNTB2WXUYWI6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised State Representation Learning in Atari","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ankesh Anand, Evan Racah, Marc-Alexandre C\\^ot\\'e, R Devon Hjelm, Sherjil Ozair, Yoshua Bengio","submitted_at":"2019-06-19T17:16:46Z","abstract_excerpt":"State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is a challenging open problem. We introduce a method that learns state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground tru"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.08226","kind":"arxiv","version":6},"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/1906.08226/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":"2026-07-05T01:49:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ibM4RkiJrEgOfXhvaHLmQ2+ZHWmTobhu9GXyFUoKt37VhNl7WwvwojKdTMxLH+nQ/0yLJHbWO7JzCpTKS579Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:13:04.511836Z"},"content_sha256":"804d00a2ff33abbe6fca06154870e3bc1384231127d380869aa4214b9ee36f77","schema_version":"1.0","event_id":"sha256:804d00a2ff33abbe6fca06154870e3bc1384231127d380869aa4214b9ee36f77"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3ZWCR3BZI2ULGKUNTB2WXUYWI6/bundle.json","state_url":"https://pith.science/pith/3ZWCR3BZI2ULGKUNTB2WXUYWI6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3ZWCR3BZI2ULGKUNTB2WXUYWI6/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":"2026-08-08T23:13:04Z","links":{"resolver":"https://pith.science/pith/3ZWCR3BZI2ULGKUNTB2WXUYWI6","bundle":"https://pith.science/pith/3ZWCR3BZI2ULGKUNTB2WXUYWI6/bundle.json","state":"https://pith.science/pith/3ZWCR3BZI2ULGKUNTB2WXUYWI6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3ZWCR3BZI2ULGKUNTB2WXUYWI6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:3ZWCR3BZI2ULGKUNTB2WXUYWI6","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":"47b4bd7bf1509f75bee06e0fe1e857cd6cd16a5d0b21e08e2b931fd01ceacdc0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-19T17:16:46Z","title_canon_sha256":"c8b701c496f09490722a2db32a0d62181cb191d14fab05aae592cec06876e452"},"schema_version":"1.0","source":{"id":"1906.08226","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.08226","created_at":"2026-07-05T01:49:34Z"},{"alias_kind":"arxiv_version","alias_value":"1906.08226v6","created_at":"2026-07-05T01:49:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.08226","created_at":"2026-07-05T01:49:34Z"},{"alias_kind":"pith_short_12","alias_value":"3ZWCR3BZI2UL","created_at":"2026-07-05T01:49:34Z"},{"alias_kind":"pith_short_16","alias_value":"3ZWCR3BZI2ULGKUN","created_at":"2026-07-05T01:49:34Z"},{"alias_kind":"pith_short_8","alias_value":"3ZWCR3BZ","created_at":"2026-07-05T01:49:34Z"}],"graph_snapshots":[{"event_id":"sha256:804d00a2ff33abbe6fca06154870e3bc1384231127d380869aa4214b9ee36f77","target":"graph","created_at":"2026-07-05T01:49:34Z","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/1906.08226/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is a challenging open problem. We introduce a method that learns state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground tru","authors_text":"Ankesh Anand, Evan Racah, Marc-Alexandre C\\^ot\\'e, R Devon Hjelm, Sherjil Ozair, Yoshua Bengio","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-19T17:16:46Z","title":"Unsupervised State Representation Learning in Atari"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.08226","kind":"arxiv","version":6},"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:a2be8cf5b182519f85621ecee71c9b516b948a01cd15a565e01e29a07f4cba3f","target":"record","created_at":"2026-07-05T01:49:34Z","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":"47b4bd7bf1509f75bee06e0fe1e857cd6cd16a5d0b21e08e2b931fd01ceacdc0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-19T17:16:46Z","title_canon_sha256":"c8b701c496f09490722a2db32a0d62181cb191d14fab05aae592cec06876e452"},"schema_version":"1.0","source":{"id":"1906.08226","kind":"arxiv","version":6}},"canonical_sha256":"de6c28ec3946a8b32a8d98756bd31647bb2fa25186e459ef220b25b8c5f462ef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"de6c28ec3946a8b32a8d98756bd31647bb2fa25186e459ef220b25b8c5f462ef","first_computed_at":"2026-07-05T01:49:34.887559Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:49:34.887559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4ZIWgKd+7n4VJRpJyIsqtsFgPzhjkswmmNUgRBdhZelW0p007ck7hQNWhO0jEMi83gERk+kY0HPKLahrF4IqBA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:49:34.888098Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.08226","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a2be8cf5b182519f85621ecee71c9b516b948a01cd15a565e01e29a07f4cba3f","sha256:804d00a2ff33abbe6fca06154870e3bc1384231127d380869aa4214b9ee36f77"],"state_sha256":"110e355d0167d50070a24a3efd5569b18e1b7b5d60ca6eee656d66151f04b727"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qBhnPauYAtVB0Z30oiywg5MJC17+S0tkAORYu7dIG7s0H4IrpQNfyEIFeh+up5+axmxuwcp7k24ZnRDH18DUBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T23:13:04.519736Z","bundle_sha256":"841ea29afff1129b229e9482c27128da79ad3ea088ebdc19564d31fa6cd824de"}}