{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:GSFPL4OAAR63BZWQZY3IKCL54J","short_pith_number":"pith:GSFPL4OA","canonical_record":{"source":{"id":"1808.05832","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-17T11:25:19Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1374c3fada40d03527315792400ecc5973b5b62c690901e8f40c0efbe6a0fd5d","abstract_canon_sha256":"b091e3510c9522e25d5545160ef2e0872950a63544a467ef163730c2be05883b"},"schema_version":"1.0"},"canonical_sha256":"348af5f1c0047db0e6d0ce3685097de2774c92f13d931d3c6496eeb330bfab0d","source":{"kind":"arxiv","id":"1808.05832","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.05832","created_at":"2026-05-18T00:07:51Z"},{"alias_kind":"arxiv_version","alias_value":"1808.05832v1","created_at":"2026-05-18T00:07:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.05832","created_at":"2026-05-18T00:07:51Z"},{"alias_kind":"pith_short_12","alias_value":"GSFPL4OAAR63","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GSFPL4OAAR63BZWQ","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GSFPL4OA","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:GSFPL4OAAR63BZWQZY3IKCL54J","target":"record","payload":{"canonical_record":{"source":{"id":"1808.05832","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-17T11:25:19Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1374c3fada40d03527315792400ecc5973b5b62c690901e8f40c0efbe6a0fd5d","abstract_canon_sha256":"b091e3510c9522e25d5545160ef2e0872950a63544a467ef163730c2be05883b"},"schema_version":"1.0"},"canonical_sha256":"348af5f1c0047db0e6d0ce3685097de2774c92f13d931d3c6496eeb330bfab0d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:51.982485Z","signature_b64":"/0JJjV2CqkaZiTy8EEwzmpADhMI2BEhVvJcmx9XvyfC1qpKT5KwSq0XZNdEAVB2V7UVtRCb1uplJMVzpiAD7CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"348af5f1c0047db0e6d0ce3685097de2774c92f13d931d3c6496eeb330bfab0d","last_reissued_at":"2026-05-18T00:07:51.981690Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:51.981690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.05832","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":"2026-05-18T00:07:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v0Vl/AAWVPcqAaQUTSdOPGGsdMCGbP9YNByqjgbxGKpS4T+s0dSFwp0p4ArbwJC8b/OcvtQmusYfBffJ2jpmCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T07:38:24.019466Z"},"content_sha256":"43afaa51d153eb4d080cb7e24c25bbd135c7074b6a3292d77437869197e743f9","schema_version":"1.0","event_id":"sha256:43afaa51d153eb4d080cb7e24c25bbd135c7074b6a3292d77437869197e743f9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:GSFPL4OAAR63BZWQZY3IKCL54J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Importance mixing: Improving sample reuse in evolutionary policy search methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alo\\\"is Pourchot, Nicolas Perrin, Olivier Sigaud","submitted_at":"2018-08-17T11:25:19Z","abstract_excerpt":"Deep neuroevolution, that is evolutionary policy search methods based on deep neural networks, have recently emerged as a competitor to deep reinforcement learning algorithms due to their better parallelization capabilities. However, these methods still suffer from a far worse sample efficiency. In this paper we investigate whether a mechanism known as \"importance mixing\" can significantly improve their sample efficiency. We provide a didactic presentation of importance mixing and we explain how it can be extended to reuse more samples. Then, from an empirical comparison based on a simple benc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.05832","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":"2026-05-18T00:07:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ncIo+5Uf0pWZlqt+XvloTBBaWafcRkFWMA8pWRAIR6W8MCn0Eif0bcZ06r8ma1sN3GwIwzcTf2gqksgh33ZMAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T07:38:24.020184Z"},"content_sha256":"fb53b0e4b13b86c16e4d4cba6ce3f878cc721609e812fa3031ae9bd083194702","schema_version":"1.0","event_id":"sha256:fb53b0e4b13b86c16e4d4cba6ce3f878cc721609e812fa3031ae9bd083194702"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GSFPL4OAAR63BZWQZY3IKCL54J/bundle.json","state_url":"https://pith.science/pith/GSFPL4OAAR63BZWQZY3IKCL54J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GSFPL4OAAR63BZWQZY3IKCL54J/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":"2026-06-11T07:38:24Z","links":{"resolver":"https://pith.science/pith/GSFPL4OAAR63BZWQZY3IKCL54J","bundle":"https://pith.science/pith/GSFPL4OAAR63BZWQZY3IKCL54J/bundle.json","state":"https://pith.science/pith/GSFPL4OAAR63BZWQZY3IKCL54J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GSFPL4OAAR63BZWQZY3IKCL54J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:GSFPL4OAAR63BZWQZY3IKCL54J","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":"b091e3510c9522e25d5545160ef2e0872950a63544a467ef163730c2be05883b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-17T11:25:19Z","title_canon_sha256":"1374c3fada40d03527315792400ecc5973b5b62c690901e8f40c0efbe6a0fd5d"},"schema_version":"1.0","source":{"id":"1808.05832","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.05832","created_at":"2026-05-18T00:07:51Z"},{"alias_kind":"arxiv_version","alias_value":"1808.05832v1","created_at":"2026-05-18T00:07:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.05832","created_at":"2026-05-18T00:07:51Z"},{"alias_kind":"pith_short_12","alias_value":"GSFPL4OAAR63","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GSFPL4OAAR63BZWQ","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GSFPL4OA","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:fb53b0e4b13b86c16e4d4cba6ce3f878cc721609e812fa3031ae9bd083194702","target":"graph","created_at":"2026-05-18T00:07:51Z","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":"Deep neuroevolution, that is evolutionary policy search methods based on deep neural networks, have recently emerged as a competitor to deep reinforcement learning algorithms due to their better parallelization capabilities. However, these methods still suffer from a far worse sample efficiency. In this paper we investigate whether a mechanism known as \"importance mixing\" can significantly improve their sample efficiency. We provide a didactic presentation of importance mixing and we explain how it can be extended to reuse more samples. Then, from an empirical comparison based on a simple benc","authors_text":"Alo\\\"is Pourchot, Nicolas Perrin, Olivier Sigaud","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-17T11:25:19Z","title":"Importance mixing: Improving sample reuse in evolutionary policy search methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.05832","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":"sha256:43afaa51d153eb4d080cb7e24c25bbd135c7074b6a3292d77437869197e743f9","target":"record","created_at":"2026-05-18T00:07:51Z","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":"b091e3510c9522e25d5545160ef2e0872950a63544a467ef163730c2be05883b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-17T11:25:19Z","title_canon_sha256":"1374c3fada40d03527315792400ecc5973b5b62c690901e8f40c0efbe6a0fd5d"},"schema_version":"1.0","source":{"id":"1808.05832","kind":"arxiv","version":1}},"canonical_sha256":"348af5f1c0047db0e6d0ce3685097de2774c92f13d931d3c6496eeb330bfab0d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"348af5f1c0047db0e6d0ce3685097de2774c92f13d931d3c6496eeb330bfab0d","first_computed_at":"2026-05-18T00:07:51.981690Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:51.981690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/0JJjV2CqkaZiTy8EEwzmpADhMI2BEhVvJcmx9XvyfC1qpKT5KwSq0XZNdEAVB2V7UVtRCb1uplJMVzpiAD7CA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:51.982485Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.05832","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:43afaa51d153eb4d080cb7e24c25bbd135c7074b6a3292d77437869197e743f9","sha256:fb53b0e4b13b86c16e4d4cba6ce3f878cc721609e812fa3031ae9bd083194702"],"state_sha256":"8879aa5d13a3a1863a8dd25f26468b26731f870bd8035f71ec9a1029b48a62fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B/ilwdoELj7wGR5uGt29jj0+ECcNOg3QjworSPx3Xaecp+0+LCQ0fnVzIuVFgGlKAOqM/0MI8Td0tla27HD4CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T07:38:24.025059Z","bundle_sha256":"10381aceca4d45cb93eb8849c01c469c3113f5337564b3e880f609ff49afd7a9"}}