{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PZGNVXFWGRXK3D2JBULBD27ISJ","short_pith_number":"pith:PZGNVXFW","schema_version":"1.0","canonical_sha256":"7e4cdadcb6346ead8f490d1611ebe892638a16eac2506c6aed76719fd5b081c7","source":{"kind":"arxiv","id":"1905.05567","version":1},"attestation_state":"computed","paper":{"title":"TauRieL: Targeting Traveling Salesman Problem with a deep reinforcement learning inspired architecture","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Adrian Cristal Kestelman, Gorker Alp Malazgirt, Osman S. Unsal","submitted_at":"2019-05-14T12:49:32Z","abstract_excerpt":"In this paper, we propose TauRieL and target Traveling Salesman Problem (TSP) since it has broad applicability in theoretical and applied sciences. TauRieL utilizes an actor-critic inspired architecture that adopts ordinary feedforward nets to obtain a policy update vector $v$. Then, we use $v$ to improve the state transition matrix from which we generate the policy. Also, the state transition matrix allows the solver to initialize from precomputed solutions such as nearest neighbors. In an online learning setting, TauRieL unifies the training and the search where it can generate near-optimal "},"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":"1905.05567","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-14T12:49:32Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"657298e30d7b4c2083f5dff7bb650fd4c8663288f8287376710a36107482e4a7","abstract_canon_sha256":"91f6e5974c3ff6e2c8ac67188c365cf32525972a1f14f27b0f60474b9fc6173f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:14.197841Z","signature_b64":"hPPzYtwmTohcuO00YpRrCcG9L6cJYupA27SXbvxe8Si4/qP7xg8uvfPplcvTY6tCgmxuhNVI7NjVbwBI4YEPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e4cdadcb6346ead8f490d1611ebe892638a16eac2506c6aed76719fd5b081c7","last_reissued_at":"2026-05-17T23:46:14.197198Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:14.197198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TauRieL: Targeting Traveling Salesman Problem with a deep reinforcement learning inspired architecture","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Adrian Cristal Kestelman, Gorker Alp Malazgirt, Osman S. Unsal","submitted_at":"2019-05-14T12:49:32Z","abstract_excerpt":"In this paper, we propose TauRieL and target Traveling Salesman Problem (TSP) since it has broad applicability in theoretical and applied sciences. TauRieL utilizes an actor-critic inspired architecture that adopts ordinary feedforward nets to obtain a policy update vector $v$. Then, we use $v$ to improve the state transition matrix from which we generate the policy. Also, the state transition matrix allows the solver to initialize from precomputed solutions such as nearest neighbors. In an online learning setting, TauRieL unifies the training and the search where it can generate near-optimal "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05567","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":"1905.05567","created_at":"2026-05-17T23:46:14.197298+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.05567v1","created_at":"2026-05-17T23:46:14.197298+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05567","created_at":"2026-05-17T23:46:14.197298+00:00"},{"alias_kind":"pith_short_12","alias_value":"PZGNVXFWGRXK","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PZGNVXFWGRXK3D2J","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PZGNVXFW","created_at":"2026-05-18T12:33:24.271573+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/PZGNVXFWGRXK3D2JBULBD27ISJ","json":"https://pith.science/pith/PZGNVXFWGRXK3D2JBULBD27ISJ.json","graph_json":"https://pith.science/api/pith-number/PZGNVXFWGRXK3D2JBULBD27ISJ/graph.json","events_json":"https://pith.science/api/pith-number/PZGNVXFWGRXK3D2JBULBD27ISJ/events.json","paper":"https://pith.science/paper/PZGNVXFW"},"agent_actions":{"view_html":"https://pith.science/pith/PZGNVXFWGRXK3D2JBULBD27ISJ","download_json":"https://pith.science/pith/PZGNVXFWGRXK3D2JBULBD27ISJ.json","view_paper":"https://pith.science/paper/PZGNVXFW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.05567&json=true","fetch_graph":"https://pith.science/api/pith-number/PZGNVXFWGRXK3D2JBULBD27ISJ/graph.json","fetch_events":"https://pith.science/api/pith-number/PZGNVXFWGRXK3D2JBULBD27ISJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PZGNVXFWGRXK3D2JBULBD27ISJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PZGNVXFWGRXK3D2JBULBD27ISJ/action/storage_attestation","attest_author":"https://pith.science/pith/PZGNVXFWGRXK3D2JBULBD27ISJ/action/author_attestation","sign_citation":"https://pith.science/pith/PZGNVXFWGRXK3D2JBULBD27ISJ/action/citation_signature","submit_replication":"https://pith.science/pith/PZGNVXFWGRXK3D2JBULBD27ISJ/action/replication_record"}},"created_at":"2026-05-17T23:46:14.197298+00:00","updated_at":"2026-05-17T23:46:14.197298+00:00"}