{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:BUB6ZTTUCYMQKU64MKR74V3P4G","short_pith_number":"pith:BUB6ZTTU","canonical_record":{"source":{"id":"1803.09001","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-23T22:40:22Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"230b86a0fc22d66deb0a07aaf74b8efdd69b8e6775b02354c3edaa552f8a611a","abstract_canon_sha256":"8dde1fcf4962175f14ac78087bcbc853dba0190fec57cdd01c3a80b78342ebfc"},"schema_version":"1.0"},"canonical_sha256":"0d03ecce7416190553dc62a3fe576fe19a2e3b4ab9c935125683f5f7a5a161d0","source":{"kind":"arxiv","id":"1803.09001","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.09001","created_at":"2026-05-18T00:20:12Z"},{"alias_kind":"arxiv_version","alias_value":"1803.09001v1","created_at":"2026-05-18T00:20:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.09001","created_at":"2026-05-18T00:20:12Z"},{"alias_kind":"pith_short_12","alias_value":"BUB6ZTTUCYMQ","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"BUB6ZTTUCYMQKU64","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"BUB6ZTTU","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:BUB6ZTTUCYMQKU64MKR74V3P4G","target":"record","payload":{"canonical_record":{"source":{"id":"1803.09001","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-23T22:40:22Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"230b86a0fc22d66deb0a07aaf74b8efdd69b8e6775b02354c3edaa552f8a611a","abstract_canon_sha256":"8dde1fcf4962175f14ac78087bcbc853dba0190fec57cdd01c3a80b78342ebfc"},"schema_version":"1.0"},"canonical_sha256":"0d03ecce7416190553dc62a3fe576fe19a2e3b4ab9c935125683f5f7a5a161d0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:12.952469Z","signature_b64":"NJKITYJQAikbaIB1pGC02e6RTvgskZ/qyLoFnwXYnyexebcwauy5FxVmQ3AUZZalZuAdUD13kbvXtkLmUi2iBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d03ecce7416190553dc62a3fe576fe19a2e3b4ab9c935125683f5f7a5a161d0","last_reissued_at":"2026-05-18T00:20:12.951875Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:12.951875Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.09001","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":"2026-05-18T00:20:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U8sLup5S2WVMFN2x/9F2tt1mQ17EUMLbFjhCV7Qs7dowY26ojHdb4Qbo9xhX4Sa0V57AKssCWYGHqphLc77LCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T03:41:30.611665Z"},"content_sha256":"583c03ad2b7e741fd22bd94a4aa6adab0a2140d2caa6ac9d2777e574036b00ed","schema_version":"1.0","event_id":"sha256:583c03ad2b7e741fd22bd94a4aa6adab0a2140d2caa6ac9d2777e574036b00ed"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:BUB6ZTTUCYMQKU64MKR74V3P4G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Craig Sherstan, Marlos C. Machado, Patrick M. Pilarski","submitted_at":"2018-03-23T22:40:22Z","abstract_excerpt":"Here we propose using the successor representation (SR) to accelerate learning in a constructive knowledge system based on general value functions (GVFs). In real-world settings like robotics for unstructured and dynamic environments, it is infeasible to model all meaningful aspects of a system and its environment by hand due to both complexity and size. Instead, robots must be capable of learning and adapting to changes in their environment and task, incrementally constructing models from their own experience. GVFs, taken from the field of reinforcement learning (RL), are a way of modeling th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.09001","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":"2026-05-18T00:20:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Yp8nGBfwzG111hyC2Bh3WUMtHhX/ttC3v83jIhJG9cDNps03F76w4QtN4Zg5/98d5cGhj0yyLep9TGlCIdEJDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T03:41:30.612405Z"},"content_sha256":"9e2cd61d6830f36d71a2ce7a062026edeaa905700a9ad0ca417623053b9389a9","schema_version":"1.0","event_id":"sha256:9e2cd61d6830f36d71a2ce7a062026edeaa905700a9ad0ca417623053b9389a9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BUB6ZTTUCYMQKU64MKR74V3P4G/bundle.json","state_url":"https://pith.science/pith/BUB6ZTTUCYMQKU64MKR74V3P4G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BUB6ZTTUCYMQKU64MKR74V3P4G/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":"2026-06-05T03:41:30Z","links":{"resolver":"https://pith.science/pith/BUB6ZTTUCYMQKU64MKR74V3P4G","bundle":"https://pith.science/pith/BUB6ZTTUCYMQKU64MKR74V3P4G/bundle.json","state":"https://pith.science/pith/BUB6ZTTUCYMQKU64MKR74V3P4G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BUB6ZTTUCYMQKU64MKR74V3P4G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:BUB6ZTTUCYMQKU64MKR74V3P4G","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":"8dde1fcf4962175f14ac78087bcbc853dba0190fec57cdd01c3a80b78342ebfc","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-23T22:40:22Z","title_canon_sha256":"230b86a0fc22d66deb0a07aaf74b8efdd69b8e6775b02354c3edaa552f8a611a"},"schema_version":"1.0","source":{"id":"1803.09001","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.09001","created_at":"2026-05-18T00:20:12Z"},{"alias_kind":"arxiv_version","alias_value":"1803.09001v1","created_at":"2026-05-18T00:20:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.09001","created_at":"2026-05-18T00:20:12Z"},{"alias_kind":"pith_short_12","alias_value":"BUB6ZTTUCYMQ","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"BUB6ZTTUCYMQKU64","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"BUB6ZTTU","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:9e2cd61d6830f36d71a2ce7a062026edeaa905700a9ad0ca417623053b9389a9","target":"graph","created_at":"2026-05-18T00:20:12Z","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":"Here we propose using the successor representation (SR) to accelerate learning in a constructive knowledge system based on general value functions (GVFs). In real-world settings like robotics for unstructured and dynamic environments, it is infeasible to model all meaningful aspects of a system and its environment by hand due to both complexity and size. Instead, robots must be capable of learning and adapting to changes in their environment and task, incrementally constructing models from their own experience. GVFs, taken from the field of reinforcement learning (RL), are a way of modeling th","authors_text":"Craig Sherstan, Marlos C. Machado, Patrick M. Pilarski","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-23T22:40:22Z","title":"Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.09001","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":"sha256:583c03ad2b7e741fd22bd94a4aa6adab0a2140d2caa6ac9d2777e574036b00ed","target":"record","created_at":"2026-05-18T00:20:12Z","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":"8dde1fcf4962175f14ac78087bcbc853dba0190fec57cdd01c3a80b78342ebfc","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-23T22:40:22Z","title_canon_sha256":"230b86a0fc22d66deb0a07aaf74b8efdd69b8e6775b02354c3edaa552f8a611a"},"schema_version":"1.0","source":{"id":"1803.09001","kind":"arxiv","version":1}},"canonical_sha256":"0d03ecce7416190553dc62a3fe576fe19a2e3b4ab9c935125683f5f7a5a161d0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0d03ecce7416190553dc62a3fe576fe19a2e3b4ab9c935125683f5f7a5a161d0","first_computed_at":"2026-05-18T00:20:12.951875Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:20:12.951875Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NJKITYJQAikbaIB1pGC02e6RTvgskZ/qyLoFnwXYnyexebcwauy5FxVmQ3AUZZalZuAdUD13kbvXtkLmUi2iBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:20:12.952469Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.09001","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:583c03ad2b7e741fd22bd94a4aa6adab0a2140d2caa6ac9d2777e574036b00ed","sha256:9e2cd61d6830f36d71a2ce7a062026edeaa905700a9ad0ca417623053b9389a9"],"state_sha256":"da6a95a8a6f8dd4014a7c561e1840ad2a9765ffa432d20dccef201cce60b0a2f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gqHJfjHOk3vEbbsmza3L21WbBkpgclj6TxXORrMU927997PXYNAfgqkKXexX6KLqhSetKxC7rJ66301aa9stCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T03:41:30.616518Z","bundle_sha256":"1e93064760d48141c6802df2513582f50afbfa56ffb48e1081a63a78b7eee8f8"}}