{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:DNJW3O4P2VTDCCQO7IX4DIKZ35","short_pith_number":"pith:DNJW3O4P","canonical_record":{"source":{"id":"1708.00463","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-08-01T18:19:40Z","cross_cats_sorted":[],"title_canon_sha256":"ce7925250299b4323fabe8b53098aa2186947bbf21903d9b04c009b666103237","abstract_canon_sha256":"7b59a22e3b275286d9307564ecc079fe24418dffa4fee58b2d905061f57312a6"},"schema_version":"1.0"},"canonical_sha256":"1b536dbb8fd566310a0efa2fc1a159df7755b7ec510e50dd9712096c5ba60724","source":{"kind":"arxiv","id":"1708.00463","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.00463","created_at":"2026-05-18T00:38:47Z"},{"alias_kind":"arxiv_version","alias_value":"1708.00463v1","created_at":"2026-05-18T00:38:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.00463","created_at":"2026-05-18T00:38:47Z"},{"alias_kind":"pith_short_12","alias_value":"DNJW3O4P2VTD","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"DNJW3O4P2VTDCCQO","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"DNJW3O4P","created_at":"2026-05-18T12:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:DNJW3O4P2VTDCCQO7IX4DIKZ35","target":"record","payload":{"canonical_record":{"source":{"id":"1708.00463","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-08-01T18:19:40Z","cross_cats_sorted":[],"title_canon_sha256":"ce7925250299b4323fabe8b53098aa2186947bbf21903d9b04c009b666103237","abstract_canon_sha256":"7b59a22e3b275286d9307564ecc079fe24418dffa4fee58b2d905061f57312a6"},"schema_version":"1.0"},"canonical_sha256":"1b536dbb8fd566310a0efa2fc1a159df7755b7ec510e50dd9712096c5ba60724","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:47.089586Z","signature_b64":"WErQbF1yTlBLvKhlC2y6maMAgGjCQDr6YAqEIFUm1lDReoZ4+DhM96GtQeuRJeMGL8MO1vRttLE91wkjm0b4Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b536dbb8fd566310a0efa2fc1a159df7755b7ec510e50dd9712096c5ba60724","last_reissued_at":"2026-05-18T00:38:47.088964Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:47.088964Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.00463","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:38:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BLBRHh8hwxZwhmqRjBvpOqg6vdCIrgAqEy5w68zjHS0p1vKQnwLPL1DbUzlb9IZzXeSm6ImdClbcmFurvbjFDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T16:36:10.402406Z"},"content_sha256":"9a1843f363e6fecc8c638316049e925cbe90eac87bf187d564d92804e5c37335","schema_version":"1.0","event_id":"sha256:9a1843f363e6fecc8c638316049e925cbe90eac87bf187d564d92804e5c37335"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:DNJW3O4P2VTDCCQO7IX4DIKZ35","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hierarchical Subtask Discovery With Non-Negative Matrix Factorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Adam C. Earle, Andrew M. Saxe, Benjamin Rosman","submitted_at":"2017-08-01T18:19:40Z","abstract_excerpt":"Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition of a domain into subtasks remains a substantial challenge. We present a novel algorithm for subtask discovery, based on the recently introduced multitask linearly-solvable Markov decision process (MLMDP) framework. The MLMDP can perform never-before-seen tasks by representing them as a linear combination of a previously learned basis set of tasks. In this setting, the subtask discovery problem can naturally be posed"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.00463","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":""},"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":"2026-05-18T00:38:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"stwumd8yovIcYClBo3nE1yyVZt+eU93V12xTYIAXP+B4Op2RvMm6ETTMIUWuvV2a+UPN+rV2tRYUS13oLbmRCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T16:36:10.403086Z"},"content_sha256":"d556dd24076a41cbdc851e6d6e32a2a2d13c53990d7a2e3b0dccf08b882b8900","schema_version":"1.0","event_id":"sha256:d556dd24076a41cbdc851e6d6e32a2a2d13c53990d7a2e3b0dccf08b882b8900"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DNJW3O4P2VTDCCQO7IX4DIKZ35/bundle.json","state_url":"https://pith.science/pith/DNJW3O4P2VTDCCQO7IX4DIKZ35/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DNJW3O4P2VTDCCQO7IX4DIKZ35/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":"2026-05-25T16:36:10Z","links":{"resolver":"https://pith.science/pith/DNJW3O4P2VTDCCQO7IX4DIKZ35","bundle":"https://pith.science/pith/DNJW3O4P2VTDCCQO7IX4DIKZ35/bundle.json","state":"https://pith.science/pith/DNJW3O4P2VTDCCQO7IX4DIKZ35/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DNJW3O4P2VTDCCQO7IX4DIKZ35/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:DNJW3O4P2VTDCCQO7IX4DIKZ35","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":"7b59a22e3b275286d9307564ecc079fe24418dffa4fee58b2d905061f57312a6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-08-01T18:19:40Z","title_canon_sha256":"ce7925250299b4323fabe8b53098aa2186947bbf21903d9b04c009b666103237"},"schema_version":"1.0","source":{"id":"1708.00463","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.00463","created_at":"2026-05-18T00:38:47Z"},{"alias_kind":"arxiv_version","alias_value":"1708.00463v1","created_at":"2026-05-18T00:38:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.00463","created_at":"2026-05-18T00:38:47Z"},{"alias_kind":"pith_short_12","alias_value":"DNJW3O4P2VTD","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"DNJW3O4P2VTDCCQO","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"DNJW3O4P","created_at":"2026-05-18T12:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:d556dd24076a41cbdc851e6d6e32a2a2d13c53990d7a2e3b0dccf08b882b8900","target":"graph","created_at":"2026-05-18T00:38:47Z","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":"Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition of a domain into subtasks remains a substantial challenge. We present a novel algorithm for subtask discovery, based on the recently introduced multitask linearly-solvable Markov decision process (MLMDP) framework. The MLMDP can perform never-before-seen tasks by representing them as a linear combination of a previously learned basis set of tasks. In this setting, the subtask discovery problem can naturally be posed","authors_text":"Adam C. Earle, Andrew M. Saxe, Benjamin Rosman","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-08-01T18:19:40Z","title":"Hierarchical Subtask Discovery With Non-Negative Matrix Factorization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.00463","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":"sha256:9a1843f363e6fecc8c638316049e925cbe90eac87bf187d564d92804e5c37335","target":"record","created_at":"2026-05-18T00:38:47Z","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":"7b59a22e3b275286d9307564ecc079fe24418dffa4fee58b2d905061f57312a6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-08-01T18:19:40Z","title_canon_sha256":"ce7925250299b4323fabe8b53098aa2186947bbf21903d9b04c009b666103237"},"schema_version":"1.0","source":{"id":"1708.00463","kind":"arxiv","version":1}},"canonical_sha256":"1b536dbb8fd566310a0efa2fc1a159df7755b7ec510e50dd9712096c5ba60724","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1b536dbb8fd566310a0efa2fc1a159df7755b7ec510e50dd9712096c5ba60724","first_computed_at":"2026-05-18T00:38:47.088964Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:47.088964Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WErQbF1yTlBLvKhlC2y6maMAgGjCQDr6YAqEIFUm1lDReoZ4+DhM96GtQeuRJeMGL8MO1vRttLE91wkjm0b4Bg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:47.089586Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.00463","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a1843f363e6fecc8c638316049e925cbe90eac87bf187d564d92804e5c37335","sha256:d556dd24076a41cbdc851e6d6e32a2a2d13c53990d7a2e3b0dccf08b882b8900"],"state_sha256":"da9da27671461c40771a9e8b859d61c9865c12353b418082e75ddca1e6398f4e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tixdUA3nGQkAezv3oR3ZdwOiEKgUZCza9GtBGmw+fABod4+Ohsguyy+T9KlWdqj0gGy/may1YNLgNerh/EN9Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T16:36:10.406394Z","bundle_sha256":"741294879fd5c6f1071f45a263b0adfc55780481c3856be4c45a5e7c4698c470"}}