{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:VY2EQ5H7K7FQ7SDCKADJXCVOSU","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":"14d23e6aad4ca0cf52ef369294347f680056aa87b254358a4b37599a2e0135e3","cross_cats_sorted":["eess.SP","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2020-01-28T05:55:55Z","title_canon_sha256":"a828cc32102684777d0142e3fa4740b0bcaa85ee9e888c639ccc5980b99149ae"},"schema_version":"1.0","source":{"id":"2001.10183","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.10183","created_at":"2026-07-05T00:36:50Z"},{"alias_kind":"arxiv_version","alias_value":"2001.10183v1","created_at":"2026-07-05T00:36:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.10183","created_at":"2026-07-05T00:36:50Z"},{"alias_kind":"pith_short_12","alias_value":"VY2EQ5H7K7FQ","created_at":"2026-07-05T00:36:50Z"},{"alias_kind":"pith_short_16","alias_value":"VY2EQ5H7K7FQ7SDC","created_at":"2026-07-05T00:36:50Z"},{"alias_kind":"pith_short_8","alias_value":"VY2EQ5H7","created_at":"2026-07-05T00:36:50Z"}],"graph_snapshots":[{"event_id":"sha256:e0794242a91864b13bddad1aed9778dcd3ea06f27463334c5ef4388cc5a970da","target":"graph","created_at":"2026-07-05T00:36:50Z","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/2001.10183/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Wireless network optimization has been becoming very challenging as the problem size and complexity increase tremendously, due to close couplings among network entities with heterogeneous service and resource requirements. By continuously interacting with the environment, deep reinforcement learning (DRL) provides a mechanism for different network entities to build knowledge and make autonomous decisions to improve network performance. In this article, we first review typical DRL approaches and recent enhancements. We then discuss the applications of DRL for mobile edge computing (MEC), which ","authors_text":"Dusit Niyato, Jing Xu, Shimin Gong, Ying-Chang Liang, Yutong Xie","cross_cats":["eess.SP","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2020-01-28T05:55:55Z","title":"Deep Reinforcement Learning for Backscatter-Aided Data Offloading in Mobile Edge Computing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.10183","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:efa65c19ee9ac8b28c63ec883d1f283501c7a89852830b8f932b0d2390386139","target":"record","created_at":"2026-07-05T00:36:50Z","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":"14d23e6aad4ca0cf52ef369294347f680056aa87b254358a4b37599a2e0135e3","cross_cats_sorted":["eess.SP","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2020-01-28T05:55:55Z","title_canon_sha256":"a828cc32102684777d0142e3fa4740b0bcaa85ee9e888c639ccc5980b99149ae"},"schema_version":"1.0","source":{"id":"2001.10183","kind":"arxiv","version":1}},"canonical_sha256":"ae344874ff57cb0fc86250069b8aae951d5b6a8c9480ab710bc8aeb2fc7b0c18","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ae344874ff57cb0fc86250069b8aae951d5b6a8c9480ab710bc8aeb2fc7b0c18","first_computed_at":"2026-07-05T00:36:50.521763Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:36:50.521763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AGUuEA/k5EYM1FwVyYbsJ/bsoLFJC3YSVYsW4Pih3P1K1GICRALRmyX8xF6IAL6zcFSes3CrrkVpQR8//1JsBA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:36:50.522150Z","signed_message":"canonical_sha256_bytes"},"source_id":"2001.10183","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:efa65c19ee9ac8b28c63ec883d1f283501c7a89852830b8f932b0d2390386139","sha256:e0794242a91864b13bddad1aed9778dcd3ea06f27463334c5ef4388cc5a970da"],"state_sha256":"2a136594988e4be30879dd0289bdd4fc9321adeab40090deb8a747c5259c4ffa"}