{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6AJ53TBKIQCC5RN35R4IMKDREJ","short_pith_number":"pith:6AJ53TBK","schema_version":"1.0","canonical_sha256":"f013ddcc2a44042ec5bbec78862871224b8dbdd391e695303a8a024716ceba35","source":{"kind":"arxiv","id":"2305.18875","version":2},"attestation_state":"computed","paper":{"title":"Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.MA","cs.SY"],"primary_cat":"eess.SY","authors_text":"Bei Peng, Flora Charbonnier, Malcolm McCulloch, Thomas Morstyn","submitted_at":"2023-05-30T09:17:09Z","abstract_excerpt":"This paper investigates how deep multi-agent reinforcement learning can enable the scalable and privacy-preserving coordination of residential energy flexibility. The coordination of distributed resources such as electric vehicles and heating will be critical to the successful integration of large shares of renewable energy in our electricity grid and, thus, to help mitigate climate change. The pre-learning of individual reinforcement learning policies can enable distributed control with no sharing of personal data required during execution. However, previous approaches for multi-agent reinfor"},"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":"2305.18875","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SY","submitted_at":"2023-05-30T09:17:09Z","cross_cats_sorted":["cs.LG","cs.MA","cs.SY"],"title_canon_sha256":"82076e0388a45566e1d9fa0643a081bececc20a670a069efcd2b05df6108395d","abstract_canon_sha256":"c78d02bc7c62da3c7362a5295fba2d858242a8f7270953b6064820e1f2edd67a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:17:29.252049Z","signature_b64":"UmNrqVTvU1AynBTwh6bgYqISTF7jkmCjI2KeE37TyZgTnzLLffn4XN9ME3l9TvlGDqdO/rwAFzfzmjh22+DdAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f013ddcc2a44042ec5bbec78862871224b8dbdd391e695303a8a024716ceba35","last_reissued_at":"2026-07-05T06:17:29.251633Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:17:29.251633Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.MA","cs.SY"],"primary_cat":"eess.SY","authors_text":"Bei Peng, Flora Charbonnier, Malcolm McCulloch, Thomas Morstyn","submitted_at":"2023-05-30T09:17:09Z","abstract_excerpt":"This paper investigates how deep multi-agent reinforcement learning can enable the scalable and privacy-preserving coordination of residential energy flexibility. The coordination of distributed resources such as electric vehicles and heating will be critical to the successful integration of large shares of renewable energy in our electricity grid and, thus, to help mitigate climate change. The pre-learning of individual reinforcement learning policies can enable distributed control with no sharing of personal data required during execution. However, previous approaches for multi-agent reinfor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.18875","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/2305.18875/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":"2305.18875","created_at":"2026-07-05T06:17:29.251688+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.18875v2","created_at":"2026-07-05T06:17:29.251688+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.18875","created_at":"2026-07-05T06:17:29.251688+00:00"},{"alias_kind":"pith_short_12","alias_value":"6AJ53TBKIQCC","created_at":"2026-07-05T06:17:29.251688+00:00"},{"alias_kind":"pith_short_16","alias_value":"6AJ53TBKIQCC5RN3","created_at":"2026-07-05T06:17:29.251688+00:00"},{"alias_kind":"pith_short_8","alias_value":"6AJ53TBK","created_at":"2026-07-05T06:17:29.251688+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/6AJ53TBKIQCC5RN35R4IMKDREJ","json":"https://pith.science/pith/6AJ53TBKIQCC5RN35R4IMKDREJ.json","graph_json":"https://pith.science/api/pith-number/6AJ53TBKIQCC5RN35R4IMKDREJ/graph.json","events_json":"https://pith.science/api/pith-number/6AJ53TBKIQCC5RN35R4IMKDREJ/events.json","paper":"https://pith.science/paper/6AJ53TBK"},"agent_actions":{"view_html":"https://pith.science/pith/6AJ53TBKIQCC5RN35R4IMKDREJ","download_json":"https://pith.science/pith/6AJ53TBKIQCC5RN35R4IMKDREJ.json","view_paper":"https://pith.science/paper/6AJ53TBK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.18875&json=true","fetch_graph":"https://pith.science/api/pith-number/6AJ53TBKIQCC5RN35R4IMKDREJ/graph.json","fetch_events":"https://pith.science/api/pith-number/6AJ53TBKIQCC5RN35R4IMKDREJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6AJ53TBKIQCC5RN35R4IMKDREJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6AJ53TBKIQCC5RN35R4IMKDREJ/action/storage_attestation","attest_author":"https://pith.science/pith/6AJ53TBKIQCC5RN35R4IMKDREJ/action/author_attestation","sign_citation":"https://pith.science/pith/6AJ53TBKIQCC5RN35R4IMKDREJ/action/citation_signature","submit_replication":"https://pith.science/pith/6AJ53TBKIQCC5RN35R4IMKDREJ/action/replication_record"}},"created_at":"2026-07-05T06:17:29.251688+00:00","updated_at":"2026-07-05T06:17:29.251688+00:00"}