{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:RIQHANYYDRBYS42R7SGAR6WL55","short_pith_number":"pith:RIQHANYY","canonical_record":{"source":{"id":"2205.09117","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T02:44:08Z","cross_cats_sorted":["cs.RO","cs.SY","eess.SY"],"title_canon_sha256":"0737c6368b11252ecc0df6902ec51cff5d611dc122d167d0c8996eb4e613c5fc","abstract_canon_sha256":"4b4c0f7dd099018dfb05598531f6aae92b997ab3682f8d7d63cf01fd534bd594"},"schema_version":"1.0"},"canonical_sha256":"8a207037181c43897351fc8c08facbef6a6482bb1292ed7992270eb05068bb9f","source":{"kind":"arxiv","id":"2205.09117","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.09117","created_at":"2026-07-05T04:24:41Z"},{"alias_kind":"arxiv_version","alias_value":"2205.09117v1","created_at":"2026-07-05T04:24:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.09117","created_at":"2026-07-05T04:24:41Z"},{"alias_kind":"pith_short_12","alias_value":"RIQHANYYDRBY","created_at":"2026-07-05T04:24:41Z"},{"alias_kind":"pith_short_16","alias_value":"RIQHANYYDRBYS42R","created_at":"2026-07-05T04:24:41Z"},{"alias_kind":"pith_short_8","alias_value":"RIQHANYY","created_at":"2026-07-05T04:24:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:RIQHANYYDRBYS42R7SGAR6WL55","target":"record","payload":{"canonical_record":{"source":{"id":"2205.09117","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T02:44:08Z","cross_cats_sorted":["cs.RO","cs.SY","eess.SY"],"title_canon_sha256":"0737c6368b11252ecc0df6902ec51cff5d611dc122d167d0c8996eb4e613c5fc","abstract_canon_sha256":"4b4c0f7dd099018dfb05598531f6aae92b997ab3682f8d7d63cf01fd534bd594"},"schema_version":"1.0"},"canonical_sha256":"8a207037181c43897351fc8c08facbef6a6482bb1292ed7992270eb05068bb9f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:24:41.272944Z","signature_b64":"iKZThUn/yYzwNmi+VUL4UiIVrxMpKETj8SlI5M+L9ZaRk43oZ/UmDl1+rcrWs9G3q8I9cpUTEd5TfJtXGSPpDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a207037181c43897351fc8c08facbef6a6482bb1292ed7992270eb05068bb9f","last_reissued_at":"2026-07-05T04:24:41.272410Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:24:41.272410Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.09117","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":"2026-07-05T04:24:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fT8HX+K2kankeg47wlO0vBNn57yQKDJQBL1Mpi4iADe9ukRg+VI7CZby3KATOSmkVqloqgp6UHfua0fcjl+rAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:44:59.437394Z"},"content_sha256":"33c11fd93427f5e1dd0838936e7df62958c99d9089f5c6ce1c5556b49ad6a15c","schema_version":"1.0","event_id":"sha256:33c11fd93427f5e1dd0838936e7df62958c99d9089f5c6ce1c5556b49ad6a15c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:RIQHANYYDRBYS42R7SGAR6WL55","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Daniela Rus, Igor Gilitschenski, Ryan Sander, Sertac Karaman, Tim Seyde, Wilko Schwarting","submitted_at":"2022-05-18T02:44:08Z","abstract_excerpt":"Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents. Recent advances in experience replay propose using Mixup (Zhang et al., 2018) to further improve sample efficiency via synthetic sample generation. We build upon this technique with Neighborhood Mixup Experience Replay (NMER), a geometrically-grounded replay buffer that interpolates transitions with their closest neighbors in state-action space. NMER preserves a locally linear approximation of the transition manifold by only applying Mixup between transitions with vicinal state-acti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.09117","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.09117/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":"2026-07-05T04:24:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5L9UqUqgb7KKC7oXL134SMRn/djFplGUiQSIId56e5QeCJuW/u7SYj0+fjR1OIdU4AnvKV/MDu72MptYen70Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:44:59.438332Z"},"content_sha256":"a2a277ce58a0f71b8029bbab76bd59e20254846fab300c48c07cf63fa1d7e7c6","schema_version":"1.0","event_id":"sha256:a2a277ce58a0f71b8029bbab76bd59e20254846fab300c48c07cf63fa1d7e7c6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RIQHANYYDRBYS42R7SGAR6WL55/bundle.json","state_url":"https://pith.science/pith/RIQHANYYDRBYS42R7SGAR6WL55/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RIQHANYYDRBYS42R7SGAR6WL55/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":"2026-08-04T12:44:59Z","links":{"resolver":"https://pith.science/pith/RIQHANYYDRBYS42R7SGAR6WL55","bundle":"https://pith.science/pith/RIQHANYYDRBYS42R7SGAR6WL55/bundle.json","state":"https://pith.science/pith/RIQHANYYDRBYS42R7SGAR6WL55/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RIQHANYYDRBYS42R7SGAR6WL55/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:RIQHANYYDRBYS42R7SGAR6WL55","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":"4b4c0f7dd099018dfb05598531f6aae92b997ab3682f8d7d63cf01fd534bd594","cross_cats_sorted":["cs.RO","cs.SY","eess.SY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T02:44:08Z","title_canon_sha256":"0737c6368b11252ecc0df6902ec51cff5d611dc122d167d0c8996eb4e613c5fc"},"schema_version":"1.0","source":{"id":"2205.09117","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.09117","created_at":"2026-07-05T04:24:41Z"},{"alias_kind":"arxiv_version","alias_value":"2205.09117v1","created_at":"2026-07-05T04:24:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.09117","created_at":"2026-07-05T04:24:41Z"},{"alias_kind":"pith_short_12","alias_value":"RIQHANYYDRBY","created_at":"2026-07-05T04:24:41Z"},{"alias_kind":"pith_short_16","alias_value":"RIQHANYYDRBYS42R","created_at":"2026-07-05T04:24:41Z"},{"alias_kind":"pith_short_8","alias_value":"RIQHANYY","created_at":"2026-07-05T04:24:41Z"}],"graph_snapshots":[{"event_id":"sha256:a2a277ce58a0f71b8029bbab76bd59e20254846fab300c48c07cf63fa1d7e7c6","target":"graph","created_at":"2026-07-05T04:24:41Z","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/2205.09117/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents. Recent advances in experience replay propose using Mixup (Zhang et al., 2018) to further improve sample efficiency via synthetic sample generation. We build upon this technique with Neighborhood Mixup Experience Replay (NMER), a geometrically-grounded replay buffer that interpolates transitions with their closest neighbors in state-action space. NMER preserves a locally linear approximation of the transition manifold by only applying Mixup between transitions with vicinal state-acti","authors_text":"Daniela Rus, Igor Gilitschenski, Ryan Sander, Sertac Karaman, Tim Seyde, Wilko Schwarting","cross_cats":["cs.RO","cs.SY","eess.SY"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T02:44:08Z","title":"Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.09117","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":"sha256:33c11fd93427f5e1dd0838936e7df62958c99d9089f5c6ce1c5556b49ad6a15c","target":"record","created_at":"2026-07-05T04:24:41Z","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":"4b4c0f7dd099018dfb05598531f6aae92b997ab3682f8d7d63cf01fd534bd594","cross_cats_sorted":["cs.RO","cs.SY","eess.SY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T02:44:08Z","title_canon_sha256":"0737c6368b11252ecc0df6902ec51cff5d611dc122d167d0c8996eb4e613c5fc"},"schema_version":"1.0","source":{"id":"2205.09117","kind":"arxiv","version":1}},"canonical_sha256":"8a207037181c43897351fc8c08facbef6a6482bb1292ed7992270eb05068bb9f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8a207037181c43897351fc8c08facbef6a6482bb1292ed7992270eb05068bb9f","first_computed_at":"2026-07-05T04:24:41.272410Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:24:41.272410Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iKZThUn/yYzwNmi+VUL4UiIVrxMpKETj8SlI5M+L9ZaRk43oZ/UmDl1+rcrWs9G3q8I9cpUTEd5TfJtXGSPpDA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:24:41.272944Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.09117","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:33c11fd93427f5e1dd0838936e7df62958c99d9089f5c6ce1c5556b49ad6a15c","sha256:a2a277ce58a0f71b8029bbab76bd59e20254846fab300c48c07cf63fa1d7e7c6"],"state_sha256":"d7c5079f78531cc8c42432dc2973ffd48442b2d4fd7fafc2a4724028200680f5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xgA725LQ5rtoyAq+BFZAIjl7R6+nqUiyvrxAamQKr2z8tyJ4d8YjHYs0fgDI+DO8nW5s87k7hG2dbbX6uSWFCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T12:44:59.445381Z","bundle_sha256":"cbb4df0ca9836b7ab0f504438ddd7fb6e8f786eb51035a0e19268a635e3102df"}}