{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:NOJBM3BSNY4MNMA4ZYEXBNQD44","short_pith_number":"pith:NOJBM3BS","schema_version":"1.0","canonical_sha256":"6b92166c326e38c6b01cce0970b603e724ec897cd1cf511c07b8e8031dcc92b1","source":{"kind":"arxiv","id":"1801.05757","version":1},"attestation_state":"computed","paper":{"title":"Experience-driven Networking: A Deep Reinforcement Learning based Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.NI","authors_text":"Chi Harold Liu, Dejun Yang, Jian Tang, Jingsong Meng, Weiyi Zhang, Yanzhi Wang, Zhiyuan Xu","submitted_at":"2018-01-17T17:09:01Z","abstract_excerpt":"Modern communication networks have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this paper, we develop a novel experience-driven approach that can learn to well control a communication network from its own experience rather than an accurate mathematical model, just as a human learns a new skill (such as driving, swimming, etc). Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in communication networks; and present a novel and highly effective DRL-based cont"},"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":"1801.05757","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2018-01-17T17:09:01Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8013bc8c9db2a2d636d7789cbf34cf259cca15142414348bf66414decf3d6729","abstract_canon_sha256":"46f147669808d41a5611f82de3d6ca3dd36cd0eec5cd56ac63ff7ca4e4cb92df"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:40.273857Z","signature_b64":"O9WrU2xbyg7yokFwuoeSZfHe+KgfIaMTYJudRHbwCVJB7bpKtEuZc6/Q5+UzvsfJNSLCBUCZUNDNxk3YMeHBBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b92166c326e38c6b01cce0970b603e724ec897cd1cf511c07b8e8031dcc92b1","last_reissued_at":"2026-05-18T00:25:40.273262Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:40.273262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Experience-driven Networking: A Deep Reinforcement Learning based Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.NI","authors_text":"Chi Harold Liu, Dejun Yang, Jian Tang, Jingsong Meng, Weiyi Zhang, Yanzhi Wang, Zhiyuan Xu","submitted_at":"2018-01-17T17:09:01Z","abstract_excerpt":"Modern communication networks have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this paper, we develop a novel experience-driven approach that can learn to well control a communication network from its own experience rather than an accurate mathematical model, just as a human learns a new skill (such as driving, swimming, etc). Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in communication networks; and present a novel and highly effective DRL-based cont"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.05757","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1801.05757","created_at":"2026-05-18T00:25:40.273344+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.05757v1","created_at":"2026-05-18T00:25:40.273344+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.05757","created_at":"2026-05-18T00:25:40.273344+00:00"},{"alias_kind":"pith_short_12","alias_value":"NOJBM3BSNY4M","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"NOJBM3BSNY4MNMA4","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"NOJBM3BS","created_at":"2026-05-18T12:32:40.477152+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/NOJBM3BSNY4MNMA4ZYEXBNQD44","json":"https://pith.science/pith/NOJBM3BSNY4MNMA4ZYEXBNQD44.json","graph_json":"https://pith.science/api/pith-number/NOJBM3BSNY4MNMA4ZYEXBNQD44/graph.json","events_json":"https://pith.science/api/pith-number/NOJBM3BSNY4MNMA4ZYEXBNQD44/events.json","paper":"https://pith.science/paper/NOJBM3BS"},"agent_actions":{"view_html":"https://pith.science/pith/NOJBM3BSNY4MNMA4ZYEXBNQD44","download_json":"https://pith.science/pith/NOJBM3BSNY4MNMA4ZYEXBNQD44.json","view_paper":"https://pith.science/paper/NOJBM3BS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.05757&json=true","fetch_graph":"https://pith.science/api/pith-number/NOJBM3BSNY4MNMA4ZYEXBNQD44/graph.json","fetch_events":"https://pith.science/api/pith-number/NOJBM3BSNY4MNMA4ZYEXBNQD44/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NOJBM3BSNY4MNMA4ZYEXBNQD44/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NOJBM3BSNY4MNMA4ZYEXBNQD44/action/storage_attestation","attest_author":"https://pith.science/pith/NOJBM3BSNY4MNMA4ZYEXBNQD44/action/author_attestation","sign_citation":"https://pith.science/pith/NOJBM3BSNY4MNMA4ZYEXBNQD44/action/citation_signature","submit_replication":"https://pith.science/pith/NOJBM3BSNY4MNMA4ZYEXBNQD44/action/replication_record"}},"created_at":"2026-05-18T00:25:40.273344+00:00","updated_at":"2026-05-18T00:25:40.273344+00:00"}