{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:4WNDXTEVVARVF7WEFSQUXVC3AF","short_pith_number":"pith:4WNDXTEV","schema_version":"1.0","canonical_sha256":"e59a3bcc95a82352fec42ca14bd45b01646cbab3617e4aefc62e745abcad8a0a","source":{"kind":"arxiv","id":"2002.07408","version":2},"attestation_state":"computed","paper":{"title":"MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Chaoqi Yang, Gongshen Liu, Guihai Chen, Haolin Zhou, Qiong Chen, Xiaofeng Gao","submitted_at":"2020-02-18T07:16:39Z","abstract_excerpt":"Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs. Considering display cost, Return on Investment (ROI), and other influential Key Performance Indicators (KPIs), large ad platforms try to balance the trade-off among various goals in dynamics. To address the challenge, we propose a Multi-ObjecTive Actor-Critics algorithm based on reinforcement learning (RL), named MoTiAC, for the problem of bidding optimization with various goals. In MoTiAC, objective-specific agents update the global network asynchrono"},"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":"2002.07408","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2020-02-18T07:16:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"de32709bf462076d7abf7617d9985d2a66eeb270e056d62b9c41616fb52cdc2a","abstract_canon_sha256":"0b034eed1faa5764dca0d6d9f25949004ccb5ce347d1f9689ec9924fb679cf77"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:37:54.096426Z","signature_b64":"5YS8mMccQag3n7/0ItvRgxzqVam1MXncwGtsM2DILuKg29Bhmbd0MmgdWlOYadc+fDhX7ljIbyJJ1jxlb2aIBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e59a3bcc95a82352fec42ca14bd45b01646cbab3617e4aefc62e745abcad8a0a","last_reissued_at":"2026-07-05T04:37:54.095990Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:37:54.095990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Chaoqi Yang, Gongshen Liu, Guihai Chen, Haolin Zhou, Qiong Chen, Xiaofeng Gao","submitted_at":"2020-02-18T07:16:39Z","abstract_excerpt":"Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs. Considering display cost, Return on Investment (ROI), and other influential Key Performance Indicators (KPIs), large ad platforms try to balance the trade-off among various goals in dynamics. To address the challenge, we propose a Multi-ObjecTive Actor-Critics algorithm based on reinforcement learning (RL), named MoTiAC, for the problem of bidding optimization with various goals. In MoTiAC, objective-specific agents update the global network asynchrono"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.07408","kind":"arxiv","version":2},"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/2002.07408/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":"2002.07408","created_at":"2026-07-05T04:37:54.096051+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.07408v2","created_at":"2026-07-05T04:37:54.096051+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.07408","created_at":"2026-07-05T04:37:54.096051+00:00"},{"alias_kind":"pith_short_12","alias_value":"4WNDXTEVVARV","created_at":"2026-07-05T04:37:54.096051+00:00"},{"alias_kind":"pith_short_16","alias_value":"4WNDXTEVVARVF7WE","created_at":"2026-07-05T04:37:54.096051+00:00"},{"alias_kind":"pith_short_8","alias_value":"4WNDXTEV","created_at":"2026-07-05T04:37:54.096051+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/4WNDXTEVVARVF7WEFSQUXVC3AF","json":"https://pith.science/pith/4WNDXTEVVARVF7WEFSQUXVC3AF.json","graph_json":"https://pith.science/api/pith-number/4WNDXTEVVARVF7WEFSQUXVC3AF/graph.json","events_json":"https://pith.science/api/pith-number/4WNDXTEVVARVF7WEFSQUXVC3AF/events.json","paper":"https://pith.science/paper/4WNDXTEV"},"agent_actions":{"view_html":"https://pith.science/pith/4WNDXTEVVARVF7WEFSQUXVC3AF","download_json":"https://pith.science/pith/4WNDXTEVVARVF7WEFSQUXVC3AF.json","view_paper":"https://pith.science/paper/4WNDXTEV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.07408&json=true","fetch_graph":"https://pith.science/api/pith-number/4WNDXTEVVARVF7WEFSQUXVC3AF/graph.json","fetch_events":"https://pith.science/api/pith-number/4WNDXTEVVARVF7WEFSQUXVC3AF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4WNDXTEVVARVF7WEFSQUXVC3AF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4WNDXTEVVARVF7WEFSQUXVC3AF/action/storage_attestation","attest_author":"https://pith.science/pith/4WNDXTEVVARVF7WEFSQUXVC3AF/action/author_attestation","sign_citation":"https://pith.science/pith/4WNDXTEVVARVF7WEFSQUXVC3AF/action/citation_signature","submit_replication":"https://pith.science/pith/4WNDXTEVVARVF7WEFSQUXVC3AF/action/replication_record"}},"created_at":"2026-07-05T04:37:54.096051+00:00","updated_at":"2026-07-05T04:37:54.096051+00:00"}