{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DS747R4JJ4QM25TFBMNFR5EDLP","short_pith_number":"pith:DS747R4J","schema_version":"1.0","canonical_sha256":"1cbfcfc7894f20cd76650b1a58f4835bee603e812044edf6b54ffb8c076a18cf","source":{"kind":"arxiv","id":"1811.06166","version":3},"attestation_state":"computed","paper":{"title":"Tiyuntsong: A Self-Play Reinforcement Learning Approach for ABR Video Streaming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Chenglei Wu, Lifeng Sun, Rui-Xiao Zhang, Tianchi Huang, Xin Yao, Zhangyuan Pang","submitted_at":"2018-11-15T04:29:49Z","abstract_excerpt":"Existing reinforcement learning~(RL)-based adaptive bitrate~(ABR) approaches outperform the previous fixed control rules based methods by improving the Quality of Experience~(QoE) score, as the QoE metric can hardly provide clear guidance for optimization, finally resulting in the unexpected strategies. In this paper, we propose \\emph{Tiyuntsong}, a self-play reinforcement learning approach with generative adversarial network~(GAN)-based method for ABR video streaming. Tiyuntsong learns strategies automatically by training two agents who are competing against each other. Note that the competit"},"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":"1811.06166","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2018-11-15T04:29:49Z","cross_cats_sorted":[],"title_canon_sha256":"28df42c6ba8f49040831f84f4774c2cf1e15d4a3aab0cc05cfcdad3b7a54d348","abstract_canon_sha256":"cc0e3b90cae6686d73693ac74bd4c40129475e33434f8fa34788b3c4fd525ea4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:12.334486Z","signature_b64":"00A2vMhvWH3zWuNOp2YBcjMFkSxUxH95VZkw52bo+rxWrpuVoNeMhw+8vFRgPT12Aa8BEUJ8azgQKMCtsrfhAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1cbfcfc7894f20cd76650b1a58f4835bee603e812044edf6b54ffb8c076a18cf","last_reissued_at":"2026-05-17T23:47:12.334016Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:12.334016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tiyuntsong: A Self-Play Reinforcement Learning Approach for ABR Video Streaming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Chenglei Wu, Lifeng Sun, Rui-Xiao Zhang, Tianchi Huang, Xin Yao, Zhangyuan Pang","submitted_at":"2018-11-15T04:29:49Z","abstract_excerpt":"Existing reinforcement learning~(RL)-based adaptive bitrate~(ABR) approaches outperform the previous fixed control rules based methods by improving the Quality of Experience~(QoE) score, as the QoE metric can hardly provide clear guidance for optimization, finally resulting in the unexpected strategies. In this paper, we propose \\emph{Tiyuntsong}, a self-play reinforcement learning approach with generative adversarial network~(GAN)-based method for ABR video streaming. Tiyuntsong learns strategies automatically by training two agents who are competing against each other. Note that the competit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.06166","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":""},"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":"1811.06166","created_at":"2026-05-17T23:47:12.334085+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.06166v3","created_at":"2026-05-17T23:47:12.334085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.06166","created_at":"2026-05-17T23:47:12.334085+00:00"},{"alias_kind":"pith_short_12","alias_value":"DS747R4JJ4QM","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DS747R4JJ4QM25TF","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DS747R4J","created_at":"2026-05-18T12:32:19.392346+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/DS747R4JJ4QM25TFBMNFR5EDLP","json":"https://pith.science/pith/DS747R4JJ4QM25TFBMNFR5EDLP.json","graph_json":"https://pith.science/api/pith-number/DS747R4JJ4QM25TFBMNFR5EDLP/graph.json","events_json":"https://pith.science/api/pith-number/DS747R4JJ4QM25TFBMNFR5EDLP/events.json","paper":"https://pith.science/paper/DS747R4J"},"agent_actions":{"view_html":"https://pith.science/pith/DS747R4JJ4QM25TFBMNFR5EDLP","download_json":"https://pith.science/pith/DS747R4JJ4QM25TFBMNFR5EDLP.json","view_paper":"https://pith.science/paper/DS747R4J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.06166&json=true","fetch_graph":"https://pith.science/api/pith-number/DS747R4JJ4QM25TFBMNFR5EDLP/graph.json","fetch_events":"https://pith.science/api/pith-number/DS747R4JJ4QM25TFBMNFR5EDLP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DS747R4JJ4QM25TFBMNFR5EDLP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DS747R4JJ4QM25TFBMNFR5EDLP/action/storage_attestation","attest_author":"https://pith.science/pith/DS747R4JJ4QM25TFBMNFR5EDLP/action/author_attestation","sign_citation":"https://pith.science/pith/DS747R4JJ4QM25TFBMNFR5EDLP/action/citation_signature","submit_replication":"https://pith.science/pith/DS747R4JJ4QM25TFBMNFR5EDLP/action/replication_record"}},"created_at":"2026-05-17T23:47:12.334085+00:00","updated_at":"2026-05-17T23:47:12.334085+00:00"}