{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:56MSTN2ZQBCBGANGM62VAGLJQF","short_pith_number":"pith:56MSTN2Z","schema_version":"1.0","canonical_sha256":"ef9929b75980441301a667b5501969814a9c8dd9133362eae51b486e5c744b42","source":{"kind":"arxiv","id":"2505.10262","version":1},"attestation_state":"computed","paper":{"title":"Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jiaju Qi, Lajos Hanzo, Lei Lei, Thorsteinn Jonsson","submitted_at":"2025-05-15T13:13:41Z","abstract_excerpt":"The charging scheduling problem of Electric Buses (EBs) is investigated based on Deep Reinforcement Learning (DRL). A Markov Decision Process (MDP) is conceived, where the time horizon includes multiple charging and operating periods in a day, while each period is further divided into multiple time steps. To overcome the challenge of long-range multi-phase planning with sparse reward, we conceive Hierarchical DRL (HDRL) for decoupling the original MDP into a high-level Semi-MDP (SMDP) and multiple low-level MDPs. The Hierarchical Double Deep Q-Network (HDDQN)-Hindsight Experience Replay (HER) "},"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":"2505.10262","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-15T13:13:41Z","cross_cats_sorted":[],"title_canon_sha256":"5fb05fdb936567d92b0af3c9de62b1422c06aaa74a52083c470f8ab4c0294c5a","abstract_canon_sha256":"3670e0305487f0e3678c8332db67e9cdfceadc9461940087dda6872aa3362e35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:03:36.104483Z","signature_b64":"nXmW+tlJwS7bZPWvoWlf9Z0PovQFPuDeyhBW83uP1m3ZDimMrYV0OFW82tL9vOycgjsHM0bTXfl9mYnFIfOWCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef9929b75980441301a667b5501969814a9c8dd9133362eae51b486e5c744b42","last_reissued_at":"2026-07-05T11:03:36.103988Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:03:36.103988Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jiaju Qi, Lajos Hanzo, Lei Lei, Thorsteinn Jonsson","submitted_at":"2025-05-15T13:13:41Z","abstract_excerpt":"The charging scheduling problem of Electric Buses (EBs) is investigated based on Deep Reinforcement Learning (DRL). A Markov Decision Process (MDP) is conceived, where the time horizon includes multiple charging and operating periods in a day, while each period is further divided into multiple time steps. To overcome the challenge of long-range multi-phase planning with sparse reward, we conceive Hierarchical DRL (HDRL) for decoupling the original MDP into a high-level Semi-MDP (SMDP) and multiple low-level MDPs. The Hierarchical Double Deep Q-Network (HDDQN)-Hindsight Experience Replay (HER) "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10262","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/2505.10262/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":"2505.10262","created_at":"2026-07-05T11:03:36.104046+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.10262v1","created_at":"2026-07-05T11:03:36.104046+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10262","created_at":"2026-07-05T11:03:36.104046+00:00"},{"alias_kind":"pith_short_12","alias_value":"56MSTN2ZQBCB","created_at":"2026-07-05T11:03:36.104046+00:00"},{"alias_kind":"pith_short_16","alias_value":"56MSTN2ZQBCBGANG","created_at":"2026-07-05T11:03:36.104046+00:00"},{"alias_kind":"pith_short_8","alias_value":"56MSTN2Z","created_at":"2026-07-05T11:03:36.104046+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/56MSTN2ZQBCBGANGM62VAGLJQF","json":"https://pith.science/pith/56MSTN2ZQBCBGANGM62VAGLJQF.json","graph_json":"https://pith.science/api/pith-number/56MSTN2ZQBCBGANGM62VAGLJQF/graph.json","events_json":"https://pith.science/api/pith-number/56MSTN2ZQBCBGANGM62VAGLJQF/events.json","paper":"https://pith.science/paper/56MSTN2Z"},"agent_actions":{"view_html":"https://pith.science/pith/56MSTN2ZQBCBGANGM62VAGLJQF","download_json":"https://pith.science/pith/56MSTN2ZQBCBGANGM62VAGLJQF.json","view_paper":"https://pith.science/paper/56MSTN2Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.10262&json=true","fetch_graph":"https://pith.science/api/pith-number/56MSTN2ZQBCBGANGM62VAGLJQF/graph.json","fetch_events":"https://pith.science/api/pith-number/56MSTN2ZQBCBGANGM62VAGLJQF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/56MSTN2ZQBCBGANGM62VAGLJQF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/56MSTN2ZQBCBGANGM62VAGLJQF/action/storage_attestation","attest_author":"https://pith.science/pith/56MSTN2ZQBCBGANGM62VAGLJQF/action/author_attestation","sign_citation":"https://pith.science/pith/56MSTN2ZQBCBGANGM62VAGLJQF/action/citation_signature","submit_replication":"https://pith.science/pith/56MSTN2ZQBCBGANGM62VAGLJQF/action/replication_record"}},"created_at":"2026-07-05T11:03:36.104046+00:00","updated_at":"2026-07-05T11:03:36.104046+00:00"}