{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:QEVDMBYQISA6DWQIAQ4B4CE6PW","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":"f3a3c8aa93aca4eb1a60a0f443c0798f8cf5886588fe2aaf680bd6a1bf116cc4","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-12-17T08:19:40Z","title_canon_sha256":"8a981ecefcad7ad24a9cb517152358f562aa422e0e158e85fb3f3a3581e860e8"},"schema_version":"1.0","source":{"id":"2412.12650","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.12650","created_at":"2026-07-05T09:50:38Z"},{"alias_kind":"arxiv_version","alias_value":"2412.12650v1","created_at":"2026-07-05T09:50:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.12650","created_at":"2026-07-05T09:50:38Z"},{"alias_kind":"pith_short_12","alias_value":"QEVDMBYQISA6","created_at":"2026-07-05T09:50:38Z"},{"alias_kind":"pith_short_16","alias_value":"QEVDMBYQISA6DWQI","created_at":"2026-07-05T09:50:38Z"},{"alias_kind":"pith_short_8","alias_value":"QEVDMBYQ","created_at":"2026-07-05T09:50:38Z"}],"graph_snapshots":[{"event_id":"sha256:74e55e6e3d11f5f4eea5f7e494706ff9e1582466b9a19f372f8a1e7bfcdd89a5","target":"graph","created_at":"2026-07-05T09:50:38Z","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/2412.12650/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Q-learning is a widely used reinforcement learning technique for solving path planning problems. It primarily involves the interaction between an agent and its environment, enabling the agent to learn an optimal strategy that maximizes cumulative rewards. Although many studies have reported the effectiveness of Q-learning, it still faces slow convergence issues in practical applications. To address this issue, we propose the NDR-QL method, which utilizes neural network outputs as heuristic information to accelerate the convergence process of Q-learning. Specifically, we improved the dual-outpu","authors_text":"Hong Liu, Kaijie Yun, Yang Liu, Yiming Ji, Zongwu Xie","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.12650","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:effb04037ea027af799ab7be475c6b01ef7775239e9e6ea3e67f51c19748c680","target":"record","created_at":"2026-07-05T09:50:38Z","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":"f3a3c8aa93aca4eb1a60a0f443c0798f8cf5886588fe2aaf680bd6a1bf116cc4","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-12-17T08:19:40Z","title_canon_sha256":"8a981ecefcad7ad24a9cb517152358f562aa422e0e158e85fb3f3a3581e860e8"},"schema_version":"1.0","source":{"id":"2412.12650","kind":"arxiv","version":1}},"canonical_sha256":"812a3607104481e1da0804381e089e7da3ebd0264fa9e1893af8775b2cd3b14e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"812a3607104481e1da0804381e089e7da3ebd0264fa9e1893af8775b2cd3b14e","first_computed_at":"2026-07-05T09:50:38.089503Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:50:38.089503Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IPNdBAlByU5Ovm1XfI1ZCT+5gR+93Xjg1Fcoj3WauUocUojOoacDVNS06zAgYU1uEf5VQZmcNyW5Go7qs+ioDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:50:38.089998Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.12650","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:effb04037ea027af799ab7be475c6b01ef7775239e9e6ea3e67f51c19748c680","sha256:74e55e6e3d11f5f4eea5f7e494706ff9e1582466b9a19f372f8a1e7bfcdd89a5"],"state_sha256":"9d222582f2c81b3859136b06650048efde7da465b0c1cd1c4b464ec7e196cd73"}