{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:SO32UZKRGQSR4ASOAH6CSN2WNK","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":"4186350646d66b40902047d4284128d7df78fffd1afbdfa089257a370417990d","cross_cats_sorted":["cs.AI","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-07-18T22:00:53Z","title_canon_sha256":"e5f1799e885262d9fc9c690336cb16373aa00a38661d41b3a62bb4ae5fd28bb5"},"schema_version":"1.0","source":{"id":"1707.05878","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.05878","created_at":"2026-05-18T00:39:59Z"},{"alias_kind":"arxiv_version","alias_value":"1707.05878v1","created_at":"2026-05-18T00:39:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.05878","created_at":"2026-05-18T00:39:59Z"},{"alias_kind":"pith_short_12","alias_value":"SO32UZKRGQSR","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SO32UZKRGQSR4ASO","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SO32UZKR","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:85c2c343b7339e55a55652483148768dc0a745c38a8f2007988669434f889420","target":"graph","created_at":"2026-05-18T00:39:59Z","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"},"paper":{"abstract_excerpt":"Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power system, and to help the customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using Deep Reinforcement Learning, a hybrid type of methods that combines Reinforcement Learning with Deep Learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using tw","authors_text":"Antonio Liotta, Decebal Constantin Mocanu, Elena Mocanu, J.G. Slootweg, Madeleine Gibescu, Michael E. Webber, Phuong H. Nguyen","cross_cats":["cs.AI","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-07-18T22:00:53Z","title":"On-line Building Energy Optimization using Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.05878","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":"sha256:9df21728b9192baa90177f763df0037f9541777c809c8d0080d7af58164efa7e","target":"record","created_at":"2026-05-18T00:39:59Z","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":"4186350646d66b40902047d4284128d7df78fffd1afbdfa089257a370417990d","cross_cats_sorted":["cs.AI","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-07-18T22:00:53Z","title_canon_sha256":"e5f1799e885262d9fc9c690336cb16373aa00a38661d41b3a62bb4ae5fd28bb5"},"schema_version":"1.0","source":{"id":"1707.05878","kind":"arxiv","version":1}},"canonical_sha256":"93b7aa655134251e024e01fc2937566ab21abd8855d4c226f2b7942258093d37","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"93b7aa655134251e024e01fc2937566ab21abd8855d4c226f2b7942258093d37","first_computed_at":"2026-05-18T00:39:59.066161Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:39:59.066161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gCegxQ/VHFx+KRRdePy7PiDv48GTLjLu4SnFWz6CvlM4ernJo67h6espjogZFz1H12ycRppRrfTk8YTTXoHdBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:39:59.066650Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.05878","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9df21728b9192baa90177f763df0037f9541777c809c8d0080d7af58164efa7e","sha256:85c2c343b7339e55a55652483148768dc0a745c38a8f2007988669434f889420"],"state_sha256":"aa8deab3638f50598fd96ca1f58d424148c2ee6fe9cda3af2bda225257dc94e4"}