{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:V2KYAFWCSP727NWPKHJZMCRDSI","short_pith_number":"pith:V2KYAFWC","schema_version":"1.0","canonical_sha256":"ae958016c293ffafb6cf51d3960a23921b4c06d1b95ee4bacf5a015f663df194","source":{"kind":"arxiv","id":"2210.13015","version":1},"attestation_state":"computed","paper":{"title":"An Opponent-Aware Reinforcement Learning Method for Team-to-Team Multi-Vehicle Pursuit via Maximizing Mutual Information Indicator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.MA","authors_text":"Chen Xu, Lin Zhang, Qinwen Wang, Xinhang Li, Yiying Yang, Zheng Yuan","submitted_at":"2022-10-24T08:04:16Z","abstract_excerpt":"The pursuit-evasion game in Smart City brings a profound impact on the Multi-vehicle Pursuit (MVP) problem, when police cars cooperatively pursue suspected vehicles. Existing studies on the MVP problems tend to set evading vehicles to move randomly or in a fixed prescribed route. The opponent modeling method has proven considerable promise in tackling the non-stationary caused by the adversary agent. However, most of them focus on two-player competitive games and easy scenarios without the interference of environments. This paper considers a Team-to-Team Multi-vehicle Pursuit (T2TMVP) problem "},"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":"2210.13015","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2022-10-24T08:04:16Z","cross_cats_sorted":[],"title_canon_sha256":"4b93b07e1148ef9968fbb06c5ea96dfd90a576c3dc0543a34c272e4a2c9dbdcd","abstract_canon_sha256":"b62df1cb0f40053ea56814e0cc9f4dc4a786a4fd743ab1a69d8519d838c36a96"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:35.255408Z","signature_b64":"/VQbQRcB7mkwws/oYrdmfZ00oOG8FHBU5dQYEMxhsdEYdKu/B0dXbu8R7r5a4sTZj6c8eCWWl6TLSnDNa96kCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae958016c293ffafb6cf51d3960a23921b4c06d1b95ee4bacf5a015f663df194","last_reissued_at":"2026-07-05T05:09:35.255012Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:35.255012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Opponent-Aware Reinforcement Learning Method for Team-to-Team Multi-Vehicle Pursuit via Maximizing Mutual Information Indicator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.MA","authors_text":"Chen Xu, Lin Zhang, Qinwen Wang, Xinhang Li, Yiying Yang, Zheng Yuan","submitted_at":"2022-10-24T08:04:16Z","abstract_excerpt":"The pursuit-evasion game in Smart City brings a profound impact on the Multi-vehicle Pursuit (MVP) problem, when police cars cooperatively pursue suspected vehicles. Existing studies on the MVP problems tend to set evading vehicles to move randomly or in a fixed prescribed route. The opponent modeling method has proven considerable promise in tackling the non-stationary caused by the adversary agent. However, most of them focus on two-player competitive games and easy scenarios without the interference of environments. This paper considers a Team-to-Team Multi-vehicle Pursuit (T2TMVP) problem "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.13015","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2210.13015/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":"2210.13015","created_at":"2026-07-05T05:09:35.255073+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.13015v1","created_at":"2026-07-05T05:09:35.255073+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.13015","created_at":"2026-07-05T05:09:35.255073+00:00"},{"alias_kind":"pith_short_12","alias_value":"V2KYAFWCSP72","created_at":"2026-07-05T05:09:35.255073+00:00"},{"alias_kind":"pith_short_16","alias_value":"V2KYAFWCSP727NWP","created_at":"2026-07-05T05:09:35.255073+00:00"},{"alias_kind":"pith_short_8","alias_value":"V2KYAFWC","created_at":"2026-07-05T05:09:35.255073+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/V2KYAFWCSP727NWPKHJZMCRDSI","json":"https://pith.science/pith/V2KYAFWCSP727NWPKHJZMCRDSI.json","graph_json":"https://pith.science/api/pith-number/V2KYAFWCSP727NWPKHJZMCRDSI/graph.json","events_json":"https://pith.science/api/pith-number/V2KYAFWCSP727NWPKHJZMCRDSI/events.json","paper":"https://pith.science/paper/V2KYAFWC"},"agent_actions":{"view_html":"https://pith.science/pith/V2KYAFWCSP727NWPKHJZMCRDSI","download_json":"https://pith.science/pith/V2KYAFWCSP727NWPKHJZMCRDSI.json","view_paper":"https://pith.science/paper/V2KYAFWC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.13015&json=true","fetch_graph":"https://pith.science/api/pith-number/V2KYAFWCSP727NWPKHJZMCRDSI/graph.json","fetch_events":"https://pith.science/api/pith-number/V2KYAFWCSP727NWPKHJZMCRDSI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V2KYAFWCSP727NWPKHJZMCRDSI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V2KYAFWCSP727NWPKHJZMCRDSI/action/storage_attestation","attest_author":"https://pith.science/pith/V2KYAFWCSP727NWPKHJZMCRDSI/action/author_attestation","sign_citation":"https://pith.science/pith/V2KYAFWCSP727NWPKHJZMCRDSI/action/citation_signature","submit_replication":"https://pith.science/pith/V2KYAFWCSP727NWPKHJZMCRDSI/action/replication_record"}},"created_at":"2026-07-05T05:09:35.255073+00:00","updated_at":"2026-07-05T05:09:35.255073+00:00"}