{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:MA7METDWR4OKB42XNG45IHWJUB","short_pith_number":"pith:MA7METDW","schema_version":"1.0","canonical_sha256":"603ec24c768f1ca0f35769b9d41ec9a04c6e92aa98814d7d195cf97cf1be252b","source":{"kind":"arxiv","id":"2006.02986","version":1},"attestation_state":"computed","paper":{"title":"A Novel Update Mechanism for Q-Networks Based On Extreme Learning Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Annalisa Riccardi, Callum Wilson, Edmondo Minisci","submitted_at":"2020-06-04T16:16:13Z","abstract_excerpt":"Reinforcement learning is a popular machine learning paradigm which can find near optimal solutions to complex problems. Most often, these procedures involve function approximation using neural networks with gradient based updates to optimise weights for the problem being considered. While this common approach generally works well, there are other update mechanisms which are largely unexplored in reinforcement learning. One such mechanism is Extreme Learning Machines. These were initially proposed to drastically improve the training speed of neural networks and have since seen many application"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2006.02986","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2020-06-04T16:16:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"53945a215f3a5348777804e8cf30110d96baea3884dbdc40d8e63ab70598622b","abstract_canon_sha256":"283809d4bf4b2c95ef8f24167c6c3dc6d542773fd5cbcb3691806be066995fbd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:08:02.213174Z","signature_b64":"Qb8KxxlkW18Ha9lFIWhSOV41uSvOxQGUWFNB9GTC6HSyyeP3gMgmcH8JQXr/PcHnnA4k5UI66ZsxOCMGTwCvDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"603ec24c768f1ca0f35769b9d41ec9a04c6e92aa98814d7d195cf97cf1be252b","last_reissued_at":"2026-07-05T01:08:02.212724Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:08:02.212724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Novel Update Mechanism for Q-Networks Based On Extreme Learning Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Annalisa Riccardi, Callum Wilson, Edmondo Minisci","submitted_at":"2020-06-04T16:16:13Z","abstract_excerpt":"Reinforcement learning is a popular machine learning paradigm which can find near optimal solutions to complex problems. Most often, these procedures involve function approximation using neural networks with gradient based updates to optimise weights for the problem being considered. While this common approach generally works well, there are other update mechanisms which are largely unexplored in reinforcement learning. One such mechanism is Extreme Learning Machines. These were initially proposed to drastically improve the training speed of neural networks and have since seen many application"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.02986","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/2006.02986/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2006.02986","created_at":"2026-07-05T01:08:02.212782+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.02986v1","created_at":"2026-07-05T01:08:02.212782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.02986","created_at":"2026-07-05T01:08:02.212782+00:00"},{"alias_kind":"pith_short_12","alias_value":"MA7METDWR4OK","created_at":"2026-07-05T01:08:02.212782+00:00"},{"alias_kind":"pith_short_16","alias_value":"MA7METDWR4OKB42X","created_at":"2026-07-05T01:08:02.212782+00:00"},{"alias_kind":"pith_short_8","alias_value":"MA7METDW","created_at":"2026-07-05T01:08:02.212782+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MA7METDWR4OKB42XNG45IHWJUB","json":"https://pith.science/pith/MA7METDWR4OKB42XNG45IHWJUB.json","graph_json":"https://pith.science/api/pith-number/MA7METDWR4OKB42XNG45IHWJUB/graph.json","events_json":"https://pith.science/api/pith-number/MA7METDWR4OKB42XNG45IHWJUB/events.json","paper":"https://pith.science/paper/MA7METDW"},"agent_actions":{"view_html":"https://pith.science/pith/MA7METDWR4OKB42XNG45IHWJUB","download_json":"https://pith.science/pith/MA7METDWR4OKB42XNG45IHWJUB.json","view_paper":"https://pith.science/paper/MA7METDW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.02986&json=true","fetch_graph":"https://pith.science/api/pith-number/MA7METDWR4OKB42XNG45IHWJUB/graph.json","fetch_events":"https://pith.science/api/pith-number/MA7METDWR4OKB42XNG45IHWJUB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MA7METDWR4OKB42XNG45IHWJUB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MA7METDWR4OKB42XNG45IHWJUB/action/storage_attestation","attest_author":"https://pith.science/pith/MA7METDWR4OKB42XNG45IHWJUB/action/author_attestation","sign_citation":"https://pith.science/pith/MA7METDWR4OKB42XNG45IHWJUB/action/citation_signature","submit_replication":"https://pith.science/pith/MA7METDWR4OKB42XNG45IHWJUB/action/replication_record"}},"created_at":"2026-07-05T01:08:02.212782+00:00","updated_at":"2026-07-05T01:08:02.212782+00:00"}