{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WXSM7OQYGVYC2SSYNWD4DIQQG2","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":"08be26aa83533a36755e0a5c3706c11ae34ee2075d458f5c99bba3ca54dd38a8","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-14T15:49:13Z","title_canon_sha256":"1e6c381cf9831494e63fdda8979a0f0936dfb21630e98a3b3c3d1a65c7a6c8f7"},"schema_version":"1.0","source":{"id":"1806.05618","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.05618","created_at":"2026-05-18T00:13:14Z"},{"alias_kind":"arxiv_version","alias_value":"1806.05618v1","created_at":"2026-05-18T00:13:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05618","created_at":"2026-05-18T00:13:14Z"},{"alias_kind":"pith_short_12","alias_value":"WXSM7OQYGVYC","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WXSM7OQYGVYC2SSY","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WXSM7OQY","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:483a8c12424baca15ece7b71a465d49a3abffedf6f193762b5167519d4db5457","target":"graph","created_at":"2026-05-18T00:13:14Z","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":"In this paper, we propose a novel reinforcement- learning algorithm consisting in a stochastic variance-reduced version of policy gradient for solving Markov Decision Processes (MDPs). Stochastic variance-reduced gradient (SVRG) methods have proven to be very successful in supervised learning. However, their adaptation to policy gradient is not straightforward and needs to account for I) a non-concave objective func- tion; II) approximations in the full gradient com- putation; and III) a non-stationary sampling pro- cess. The result is SVRPG, a stochastic variance- reduced policy gradient algo","authors_text":"Damiano Binaghi, Giuseppe Canonaco, Marcello Restelli, Matteo Papini, Matteo Pirotta","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-14T15:49:13Z","title":"Stochastic Variance-Reduced Policy Gradient"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05618","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":"sha256:513aeb5521fe79a2f31c927a9eb069f322244bd206df7035e68602ece6657d5f","target":"record","created_at":"2026-05-18T00:13:14Z","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":"08be26aa83533a36755e0a5c3706c11ae34ee2075d458f5c99bba3ca54dd38a8","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-14T15:49:13Z","title_canon_sha256":"1e6c381cf9831494e63fdda8979a0f0936dfb21630e98a3b3c3d1a65c7a6c8f7"},"schema_version":"1.0","source":{"id":"1806.05618","kind":"arxiv","version":1}},"canonical_sha256":"b5e4cfba1835702d4a586d87c1a21036a09f2c6baea1fe8a21d12034f29436fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b5e4cfba1835702d4a586d87c1a21036a09f2c6baea1fe8a21d12034f29436fa","first_computed_at":"2026-05-18T00:13:14.582840Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:14.582840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5xjHyj1oUkNxNySIBD6EWDBtbPmzPeCJG/JgtBo/ilAD0bB8/2o4huVcR8UyY+XOWQxSrzmdqUrGZ0gUyHrXBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:14.583488Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.05618","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:513aeb5521fe79a2f31c927a9eb069f322244bd206df7035e68602ece6657d5f","sha256:483a8c12424baca15ece7b71a465d49a3abffedf6f193762b5167519d4db5457"],"state_sha256":"9a695a9ae44868c1b298b37a1bbff44e978db811a989fea3319670260499f53e"}