{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:RG4SMIOO7S7VTXE7BAGUZFSLV4","short_pith_number":"pith:RG4SMIOO","canonical_record":{"source":{"id":"1901.01994","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-06T23:35:07Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"4a44ce5b9aa947f25a5563f8cb47a58a9ece938fdb8855947a9fe32d46ac2afd","abstract_canon_sha256":"4828154130c964ecc2430b51ebed60ad2f9b4639bb55f785fc7a623637c56bdb"},"schema_version":"1.0"},"canonical_sha256":"89b92621cefcbf59dc9f080d4c964baf3dc7b474b87493b0e687dae5f790b160","source":{"kind":"arxiv","id":"1901.01994","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.01994","created_at":"2026-05-17T23:56:04Z"},{"alias_kind":"arxiv_version","alias_value":"1901.01994v2","created_at":"2026-05-17T23:56:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.01994","created_at":"2026-05-17T23:56:04Z"},{"alias_kind":"pith_short_12","alias_value":"RG4SMIOO7S7V","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"RG4SMIOO7S7VTXE7","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"RG4SMIOO","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:RG4SMIOO7S7VTXE7BAGUZFSLV4","target":"record","payload":{"canonical_record":{"source":{"id":"1901.01994","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-06T23:35:07Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"4a44ce5b9aa947f25a5563f8cb47a58a9ece938fdb8855947a9fe32d46ac2afd","abstract_canon_sha256":"4828154130c964ecc2430b51ebed60ad2f9b4639bb55f785fc7a623637c56bdb"},"schema_version":"1.0"},"canonical_sha256":"89b92621cefcbf59dc9f080d4c964baf3dc7b474b87493b0e687dae5f790b160","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:04.968975Z","signature_b64":"Lw9w34zAxjhJG+gSGrcgZQxZZw1UM6Ch2QXvno1BGeDrbp7NQVFxlTDcVxc0c4c1a8YbX+N39lX6HSwYbOzBDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89b92621cefcbf59dc9f080d4c964baf3dc7b474b87493b0e687dae5f790b160","last_reissued_at":"2026-05-17T23:56:04.968468Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:04.968468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.01994","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:56:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hjo24nVogbnEXp9vizBvr2S2smD6M3XNVO2UsMM+BmEzhKsQSATGiZUfmtYyQXjMhI6EiI47ET6kFyEXud5ADA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T11:43:05.353964Z"},"content_sha256":"dbee26146327597856b635b9c0649af47c63cc319bbcd0dad4b590a8b29eda00","schema_version":"1.0","event_id":"sha256:dbee26146327597856b635b9c0649af47c63cc319bbcd0dad4b590a8b29eda00"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:RG4SMIOO7S7VTXE7BAGUZFSLV4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recurrent Control Nets for Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ademi Adeniji, Jason Zhao, Mario Srouji, Nathaniel Lee, Vincent Liu","submitted_at":"2019-01-06T23:35:07Z","abstract_excerpt":"Central Pattern Generators (CPGs) are biological neural circuits capable of producing coordinated rhythmic outputs in the absence of rhythmic input. As a result, they are responsible for most rhythmic motion in living organisms. This rhythmic control is broadly applicable to fields such as locomotive robotics and medical devices. In this paper, we explore the possibility of creating a self-sustaining CPG network for reinforcement learning that learns rhythmic motion more efficiently and across more general environments than the current multilayer perceptron (MLP) baseline models. Recent work i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.01994","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:56:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lTwRlX8TXojL6jPMRrp8u/Yt30vxHOtOeS+Zn7lwKymnW3zgjENDuL3NogprcXNuojIIi2nQlcIKLxRjPsWeDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T11:43:05.354349Z"},"content_sha256":"20dc730ce86ea896d41ac628cc7328efee590761c7ace8ac6537a3c29064622f","schema_version":"1.0","event_id":"sha256:20dc730ce86ea896d41ac628cc7328efee590761c7ace8ac6537a3c29064622f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RG4SMIOO7S7VTXE7BAGUZFSLV4/bundle.json","state_url":"https://pith.science/pith/RG4SMIOO7S7VTXE7BAGUZFSLV4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RG4SMIOO7S7VTXE7BAGUZFSLV4/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T11:43:05Z","links":{"resolver":"https://pith.science/pith/RG4SMIOO7S7VTXE7BAGUZFSLV4","bundle":"https://pith.science/pith/RG4SMIOO7S7VTXE7BAGUZFSLV4/bundle.json","state":"https://pith.science/pith/RG4SMIOO7S7VTXE7BAGUZFSLV4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RG4SMIOO7S7VTXE7BAGUZFSLV4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:RG4SMIOO7S7VTXE7BAGUZFSLV4","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":"4828154130c964ecc2430b51ebed60ad2f9b4639bb55f785fc7a623637c56bdb","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-06T23:35:07Z","title_canon_sha256":"4a44ce5b9aa947f25a5563f8cb47a58a9ece938fdb8855947a9fe32d46ac2afd"},"schema_version":"1.0","source":{"id":"1901.01994","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.01994","created_at":"2026-05-17T23:56:04Z"},{"alias_kind":"arxiv_version","alias_value":"1901.01994v2","created_at":"2026-05-17T23:56:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.01994","created_at":"2026-05-17T23:56:04Z"},{"alias_kind":"pith_short_12","alias_value":"RG4SMIOO7S7V","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"RG4SMIOO7S7VTXE7","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"RG4SMIOO","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:20dc730ce86ea896d41ac628cc7328efee590761c7ace8ac6537a3c29064622f","target":"graph","created_at":"2026-05-17T23:56:04Z","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":"Central Pattern Generators (CPGs) are biological neural circuits capable of producing coordinated rhythmic outputs in the absence of rhythmic input. As a result, they are responsible for most rhythmic motion in living organisms. This rhythmic control is broadly applicable to fields such as locomotive robotics and medical devices. In this paper, we explore the possibility of creating a self-sustaining CPG network for reinforcement learning that learns rhythmic motion more efficiently and across more general environments than the current multilayer perceptron (MLP) baseline models. Recent work i","authors_text":"Ademi Adeniji, Jason Zhao, Mario Srouji, Nathaniel Lee, Vincent Liu","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-06T23:35:07Z","title":"Recurrent Control Nets for Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.01994","kind":"arxiv","version":2},"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:dbee26146327597856b635b9c0649af47c63cc319bbcd0dad4b590a8b29eda00","target":"record","created_at":"2026-05-17T23:56:04Z","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":"4828154130c964ecc2430b51ebed60ad2f9b4639bb55f785fc7a623637c56bdb","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-06T23:35:07Z","title_canon_sha256":"4a44ce5b9aa947f25a5563f8cb47a58a9ece938fdb8855947a9fe32d46ac2afd"},"schema_version":"1.0","source":{"id":"1901.01994","kind":"arxiv","version":2}},"canonical_sha256":"89b92621cefcbf59dc9f080d4c964baf3dc7b474b87493b0e687dae5f790b160","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"89b92621cefcbf59dc9f080d4c964baf3dc7b474b87493b0e687dae5f790b160","first_computed_at":"2026-05-17T23:56:04.968468Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:56:04.968468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Lw9w34zAxjhJG+gSGrcgZQxZZw1UM6Ch2QXvno1BGeDrbp7NQVFxlTDcVxc0c4c1a8YbX+N39lX6HSwYbOzBDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:56:04.968975Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.01994","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dbee26146327597856b635b9c0649af47c63cc319bbcd0dad4b590a8b29eda00","sha256:20dc730ce86ea896d41ac628cc7328efee590761c7ace8ac6537a3c29064622f"],"state_sha256":"f308e5c30ef82bd2047042a15dfd95499ab1dfe0d45fdeaa93797a1f304c8df6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bl4sTGhSEhnE2dK0rzAlKju5SuiqQseMUpenJ4EzV2ohrs5yfvx/IVFE+NFyADymuyb/YNSPTvah0aTKo1UPBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T11:43:05.358252Z","bundle_sha256":"09ebfd798e0d4cd23e191003299ee4feeec42748eda32f5b406f4a8524a16669"}}