{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VNIXBVTFLSP4ZN6F3JFQT2U7KJ","short_pith_number":"pith:VNIXBVTF","schema_version":"1.0","canonical_sha256":"ab5170d6655c9fccb7c5da4b09ea9f526075064499a5b956afd6bb73b8d39e6d","source":{"kind":"arxiv","id":"2412.02057","version":2},"attestation_state":"computed","paper":{"title":"Comparative Analysis of Multi-Agent Reinforcement Learning Policies for Crop Planning Decision Support","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.LG","authors_text":"Anubha Mahajan, Aviva Prins, Daniel Wu, Ethan Shay, Shreya Hegde","submitted_at":"2024-12-03T00:30:19Z","abstract_excerpt":"In India, the majority of farmers are classified as small or marginal, making their livelihoods particularly vulnerable to economic losses due to market saturation and climate risks. Effective crop planning can significantly impact their expected income, yet existing decision support systems (DSS) often provide generic recommendations that fail to account for real-time market dynamics and the interactions among multiple farmers. In this paper, we evaluate the viability of three multi-agent reinforcement learning (MARL) approaches for optimizing total farmer income and promoting fairness in cro"},"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":"2412.02057","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-03T00:30:19Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"137888de99226883f89d2dd4352e6c6faa71b6fb6a744d1e63d8fab83a7b07dd","abstract_canon_sha256":"4b243b3cfa126fa1b564679baba76815f3b023001111cf41d2a3f31abe72525c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:18:45.947377Z","signature_b64":"JkjTDoR1WRqCVvSVVygngju/3p8F1f1oSS/eXZzraYPDx6M6hxfGreO02OPPF3wA9ETBXe+j7blW4P2pw9nuAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab5170d6655c9fccb7c5da4b09ea9f526075064499a5b956afd6bb73b8d39e6d","last_reissued_at":"2026-07-05T10:18:45.946753Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:18:45.946753Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comparative Analysis of Multi-Agent Reinforcement Learning Policies for Crop Planning Decision Support","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.LG","authors_text":"Anubha Mahajan, Aviva Prins, Daniel Wu, Ethan Shay, Shreya Hegde","submitted_at":"2024-12-03T00:30:19Z","abstract_excerpt":"In India, the majority of farmers are classified as small or marginal, making their livelihoods particularly vulnerable to economic losses due to market saturation and climate risks. Effective crop planning can significantly impact their expected income, yet existing decision support systems (DSS) often provide generic recommendations that fail to account for real-time market dynamics and the interactions among multiple farmers. In this paper, we evaluate the viability of three multi-agent reinforcement learning (MARL) approaches for optimizing total farmer income and promoting fairness in cro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.02057","kind":"arxiv","version":2},"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/2412.02057/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":"2412.02057","created_at":"2026-07-05T10:18:45.946821+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.02057v2","created_at":"2026-07-05T10:18:45.946821+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.02057","created_at":"2026-07-05T10:18:45.946821+00:00"},{"alias_kind":"pith_short_12","alias_value":"VNIXBVTFLSP4","created_at":"2026-07-05T10:18:45.946821+00:00"},{"alias_kind":"pith_short_16","alias_value":"VNIXBVTFLSP4ZN6F","created_at":"2026-07-05T10:18:45.946821+00:00"},{"alias_kind":"pith_short_8","alias_value":"VNIXBVTF","created_at":"2026-07-05T10:18:45.946821+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/VNIXBVTFLSP4ZN6F3JFQT2U7KJ","json":"https://pith.science/pith/VNIXBVTFLSP4ZN6F3JFQT2U7KJ.json","graph_json":"https://pith.science/api/pith-number/VNIXBVTFLSP4ZN6F3JFQT2U7KJ/graph.json","events_json":"https://pith.science/api/pith-number/VNIXBVTFLSP4ZN6F3JFQT2U7KJ/events.json","paper":"https://pith.science/paper/VNIXBVTF"},"agent_actions":{"view_html":"https://pith.science/pith/VNIXBVTFLSP4ZN6F3JFQT2U7KJ","download_json":"https://pith.science/pith/VNIXBVTFLSP4ZN6F3JFQT2U7KJ.json","view_paper":"https://pith.science/paper/VNIXBVTF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.02057&json=true","fetch_graph":"https://pith.science/api/pith-number/VNIXBVTFLSP4ZN6F3JFQT2U7KJ/graph.json","fetch_events":"https://pith.science/api/pith-number/VNIXBVTFLSP4ZN6F3JFQT2U7KJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VNIXBVTFLSP4ZN6F3JFQT2U7KJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VNIXBVTFLSP4ZN6F3JFQT2U7KJ/action/storage_attestation","attest_author":"https://pith.science/pith/VNIXBVTFLSP4ZN6F3JFQT2U7KJ/action/author_attestation","sign_citation":"https://pith.science/pith/VNIXBVTFLSP4ZN6F3JFQT2U7KJ/action/citation_signature","submit_replication":"https://pith.science/pith/VNIXBVTFLSP4ZN6F3JFQT2U7KJ/action/replication_record"}},"created_at":"2026-07-05T10:18:45.946821+00:00","updated_at":"2026-07-05T10:18:45.946821+00:00"}