{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GAZDIJQ765YVSFX4AINPALYBSZ","short_pith_number":"pith:GAZDIJQ7","schema_version":"1.0","canonical_sha256":"303234261ff7715916fc021af02f019655638e23442b924bd37f0b3021d6ad0e","source":{"kind":"arxiv","id":"2508.03647","version":1},"attestation_state":"computed","paper":{"title":"Improving Q-Learning for Real-World Control: A Case Study in Series Hybrid Agricultural Tractors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Hend Abououf, Qadeer Ahmed, Sidra Ghayour Bhatti","submitted_at":"2025-08-05T16:57:16Z","abstract_excerpt":"The variable and unpredictable load demands in hybrid agricultural tractors make it difficult to design optimal rule-based energy management strategies, motivating the use of adaptive, learning-based control. However, existing approaches often rely on basic fuel-based rewards and do not leverage expert demonstrations to accelerate training. In this paper, first, the performance of Q-value-based reinforcement learning algorithms is evaluated for powertrain control in a hybrid agricultural tractor. Three algorithms, Double Q-Learning (DQL), Deep Q-Networks (DQN), and Double DQN (DDQN), are compa"},"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":"2508.03647","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2025-08-05T16:57:16Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"77eed7e74d9c99f0d507d0b1698690c93df1017578f461666eab413439789989","abstract_canon_sha256":"942837c8edab2debe9a395ff22d31892f1af7c596ac6bb74857012df0f1b805c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:00.512733Z","signature_b64":"Ph7XkyYysN4KmDNXdHvmgheKNHnLr/4Oi1UMwofQU7Zdx5winW6g5LtzFomrdRhWhfiX0fAmuNACDRkFRsqVAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"303234261ff7715916fc021af02f019655638e23442b924bd37f0b3021d6ad0e","last_reissued_at":"2026-07-05T11:49:00.512298Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:00.512298Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Q-Learning for Real-World Control: A Case Study in Series Hybrid Agricultural Tractors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Hend Abououf, Qadeer Ahmed, Sidra Ghayour Bhatti","submitted_at":"2025-08-05T16:57:16Z","abstract_excerpt":"The variable and unpredictable load demands in hybrid agricultural tractors make it difficult to design optimal rule-based energy management strategies, motivating the use of adaptive, learning-based control. However, existing approaches often rely on basic fuel-based rewards and do not leverage expert demonstrations to accelerate training. In this paper, first, the performance of Q-value-based reinforcement learning algorithms is evaluated for powertrain control in a hybrid agricultural tractor. Three algorithms, Double Q-Learning (DQL), Deep Q-Networks (DQN), and Double DQN (DDQN), are compa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.03647","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/2508.03647/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":"2508.03647","created_at":"2026-07-05T11:49:00.512365+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.03647v1","created_at":"2026-07-05T11:49:00.512365+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.03647","created_at":"2026-07-05T11:49:00.512365+00:00"},{"alias_kind":"pith_short_12","alias_value":"GAZDIJQ765YV","created_at":"2026-07-05T11:49:00.512365+00:00"},{"alias_kind":"pith_short_16","alias_value":"GAZDIJQ765YVSFX4","created_at":"2026-07-05T11:49:00.512365+00:00"},{"alias_kind":"pith_short_8","alias_value":"GAZDIJQ7","created_at":"2026-07-05T11:49:00.512365+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/GAZDIJQ765YVSFX4AINPALYBSZ","json":"https://pith.science/pith/GAZDIJQ765YVSFX4AINPALYBSZ.json","graph_json":"https://pith.science/api/pith-number/GAZDIJQ765YVSFX4AINPALYBSZ/graph.json","events_json":"https://pith.science/api/pith-number/GAZDIJQ765YVSFX4AINPALYBSZ/events.json","paper":"https://pith.science/paper/GAZDIJQ7"},"agent_actions":{"view_html":"https://pith.science/pith/GAZDIJQ765YVSFX4AINPALYBSZ","download_json":"https://pith.science/pith/GAZDIJQ765YVSFX4AINPALYBSZ.json","view_paper":"https://pith.science/paper/GAZDIJQ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.03647&json=true","fetch_graph":"https://pith.science/api/pith-number/GAZDIJQ765YVSFX4AINPALYBSZ/graph.json","fetch_events":"https://pith.science/api/pith-number/GAZDIJQ765YVSFX4AINPALYBSZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GAZDIJQ765YVSFX4AINPALYBSZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GAZDIJQ765YVSFX4AINPALYBSZ/action/storage_attestation","attest_author":"https://pith.science/pith/GAZDIJQ765YVSFX4AINPALYBSZ/action/author_attestation","sign_citation":"https://pith.science/pith/GAZDIJQ765YVSFX4AINPALYBSZ/action/citation_signature","submit_replication":"https://pith.science/pith/GAZDIJQ765YVSFX4AINPALYBSZ/action/replication_record"}},"created_at":"2026-07-05T11:49:00.512365+00:00","updated_at":"2026-07-05T11:49:00.512365+00:00"}