{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:NKSRFOH2OH6CNU6Z26XZZCZSNR","short_pith_number":"pith:NKSRFOH2","canonical_record":{"source":{"id":"2006.07262","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-12T15:17:17Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"4ea39c1594ae73c71aee1a542505acd5713a12be0882ff0b772d1350434d9f54","abstract_canon_sha256":"7ff91930f0846b5609b83cdfe6fc4b5ad9f0708b1fa5792d6db3dfacd4e237a4"},"schema_version":"1.0"},"canonical_sha256":"6aa512b8fa71fc26d3d9d7af9c8b326c6f5a0bcb1f009488129d44fda6ccf285","source":{"kind":"arxiv","id":"2006.07262","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.07262","created_at":"2026-07-05T01:15:59Z"},{"alias_kind":"arxiv_version","alias_value":"2006.07262v3","created_at":"2026-07-05T01:15:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.07262","created_at":"2026-07-05T01:15:59Z"},{"alias_kind":"pith_short_12","alias_value":"NKSRFOH2OH6C","created_at":"2026-07-05T01:15:59Z"},{"alias_kind":"pith_short_16","alias_value":"NKSRFOH2OH6CNU6Z","created_at":"2026-07-05T01:15:59Z"},{"alias_kind":"pith_short_8","alias_value":"NKSRFOH2","created_at":"2026-07-05T01:15:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:NKSRFOH2OH6CNU6Z26XZZCZSNR","target":"record","payload":{"canonical_record":{"source":{"id":"2006.07262","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-12T15:17:17Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"4ea39c1594ae73c71aee1a542505acd5713a12be0882ff0b772d1350434d9f54","abstract_canon_sha256":"7ff91930f0846b5609b83cdfe6fc4b5ad9f0708b1fa5792d6db3dfacd4e237a4"},"schema_version":"1.0"},"canonical_sha256":"6aa512b8fa71fc26d3d9d7af9c8b326c6f5a0bcb1f009488129d44fda6ccf285","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:15:59.389279Z","signature_b64":"yeAJTZ+RvvHC9hUUIrAxiiLjFh5/sqcaJP32YLisoP9c29N0YUNrz0nbBX7bNKzu36EmfV1Zf8fwzVYT3eUkDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6aa512b8fa71fc26d3d9d7af9c8b326c6f5a0bcb1f009488129d44fda6ccf285","last_reissued_at":"2026-07-05T01:15:59.388825Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:15:59.388825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.07262","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:15:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qg4SSMos5S402Q/8WxzqGwLag0ueztRAPC7S02zFX7O8dPhR8XefFGIpwyEPpAnNSN6qtAzr4ASexaU8cPjcBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T23:46:27.756377Z"},"content_sha256":"68cde19e45af855b08e2f5ec4055a184d3e3f509c52119ca6676c011985b731d","schema_version":"1.0","event_id":"sha256:68cde19e45af855b08e2f5ec4055a184d3e3f509c52119ca6676c011985b731d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:NKSRFOH2OH6CNU6Z26XZZCZSNR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Brief Look at Generalization in Visual Meta-Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Doina Precup, Safa Alver","submitted_at":"2020-06-12T15:17:17Z","abstract_excerpt":"Due to the realization that deep reinforcement learning algorithms trained on high-dimensional tasks can strongly overfit to their training environments, there have been several studies that investigated the generalization performance of these algorithms. However, there has been no similar study that evaluated the generalization performance of algorithms that were specifically designed for generalization, i.e. meta-reinforcement learning algorithms. In this paper, we assess the generalization performance of these algorithms by leveraging high-dimensional, procedurally generated environments. W"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.07262","kind":"arxiv","version":3},"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/2006.07262/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":"2026-07-05T01:15:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oAqCoKFkcSKwYFY5AynLXdnvnPo0dDddPcwo+nLdtmYv9BFwl7JOhJ49ad+7L1ae/vrDPq02Qr+XZyD4TXrbCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T23:46:27.756888Z"},"content_sha256":"204ae7c5d6630882c4a526861892aeda99287f81aa230a35fc46c2a422c329d6","schema_version":"1.0","event_id":"sha256:204ae7c5d6630882c4a526861892aeda99287f81aa230a35fc46c2a422c329d6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NKSRFOH2OH6CNU6Z26XZZCZSNR/bundle.json","state_url":"https://pith.science/pith/NKSRFOH2OH6CNU6Z26XZZCZSNR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NKSRFOH2OH6CNU6Z26XZZCZSNR/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":"2026-08-19T23:46:27Z","links":{"resolver":"https://pith.science/pith/NKSRFOH2OH6CNU6Z26XZZCZSNR","bundle":"https://pith.science/pith/NKSRFOH2OH6CNU6Z26XZZCZSNR/bundle.json","state":"https://pith.science/pith/NKSRFOH2OH6CNU6Z26XZZCZSNR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NKSRFOH2OH6CNU6Z26XZZCZSNR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:NKSRFOH2OH6CNU6Z26XZZCZSNR","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":"7ff91930f0846b5609b83cdfe6fc4b5ad9f0708b1fa5792d6db3dfacd4e237a4","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-12T15:17:17Z","title_canon_sha256":"4ea39c1594ae73c71aee1a542505acd5713a12be0882ff0b772d1350434d9f54"},"schema_version":"1.0","source":{"id":"2006.07262","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.07262","created_at":"2026-07-05T01:15:59Z"},{"alias_kind":"arxiv_version","alias_value":"2006.07262v3","created_at":"2026-07-05T01:15:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.07262","created_at":"2026-07-05T01:15:59Z"},{"alias_kind":"pith_short_12","alias_value":"NKSRFOH2OH6C","created_at":"2026-07-05T01:15:59Z"},{"alias_kind":"pith_short_16","alias_value":"NKSRFOH2OH6CNU6Z","created_at":"2026-07-05T01:15:59Z"},{"alias_kind":"pith_short_8","alias_value":"NKSRFOH2","created_at":"2026-07-05T01:15:59Z"}],"graph_snapshots":[{"event_id":"sha256:204ae7c5d6630882c4a526861892aeda99287f81aa230a35fc46c2a422c329d6","target":"graph","created_at":"2026-07-05T01:15:59Z","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/2006.07262/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Due to the realization that deep reinforcement learning algorithms trained on high-dimensional tasks can strongly overfit to their training environments, there have been several studies that investigated the generalization performance of these algorithms. However, there has been no similar study that evaluated the generalization performance of algorithms that were specifically designed for generalization, i.e. meta-reinforcement learning algorithms. In this paper, we assess the generalization performance of these algorithms by leveraging high-dimensional, procedurally generated environments. W","authors_text":"Doina Precup, Safa Alver","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-12T15:17:17Z","title":"A Brief Look at Generalization in Visual Meta-Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.07262","kind":"arxiv","version":3},"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":"sha256:68cde19e45af855b08e2f5ec4055a184d3e3f509c52119ca6676c011985b731d","target":"record","created_at":"2026-07-05T01:15:59Z","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":"7ff91930f0846b5609b83cdfe6fc4b5ad9f0708b1fa5792d6db3dfacd4e237a4","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-12T15:17:17Z","title_canon_sha256":"4ea39c1594ae73c71aee1a542505acd5713a12be0882ff0b772d1350434d9f54"},"schema_version":"1.0","source":{"id":"2006.07262","kind":"arxiv","version":3}},"canonical_sha256":"6aa512b8fa71fc26d3d9d7af9c8b326c6f5a0bcb1f009488129d44fda6ccf285","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6aa512b8fa71fc26d3d9d7af9c8b326c6f5a0bcb1f009488129d44fda6ccf285","first_computed_at":"2026-07-05T01:15:59.388825Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:15:59.388825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yeAJTZ+RvvHC9hUUIrAxiiLjFh5/sqcaJP32YLisoP9c29N0YUNrz0nbBX7bNKzu36EmfV1Zf8fwzVYT3eUkDg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:15:59.389279Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.07262","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:68cde19e45af855b08e2f5ec4055a184d3e3f509c52119ca6676c011985b731d","sha256:204ae7c5d6630882c4a526861892aeda99287f81aa230a35fc46c2a422c329d6"],"state_sha256":"4fab3be54e8f8d5d1de961e5228c13b856cf329c42ae7b6546694d7f1e4dd2c8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"54GcniWNDyXd9yJG3sCJhgyaBtpQv0PEUAk5hxdeMcGreUJQD03/ag2GYkdPtLsMtxx65VyHZubcFcgcM3GkCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T23:46:27.760809Z","bundle_sha256":"ce92d0d676f09e3e8d7646c6e5594aafa7230f9929378e81405652be4f66ff79"}}