{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:A6BQ6T7FTDKQBS4S6WARUUT6TP","short_pith_number":"pith:A6BQ6T7F","canonical_record":{"source":{"id":"2006.15009","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-26T14:30:41Z","cross_cats_sorted":["cs.AI","cs.RO","stat.ML"],"title_canon_sha256":"949a33da0558d24dc89f0b7abf7e9c1d925a4a355678b0fc1e1edaa97f106b0b","abstract_canon_sha256":"8cada46b95507c946c1d2221e8cfb286102b86889f5210b54c14c4b2a640b4a3"},"schema_version":"1.0"},"canonical_sha256":"07830f4fe598d500cb92f5811a527e9bd526ecf262bd7bc98b97cf7a88995ee1","source":{"kind":"arxiv","id":"2006.15009","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.15009","created_at":"2026-07-05T04:10:20Z"},{"alias_kind":"arxiv_version","alias_value":"2006.15009v4","created_at":"2026-07-05T04:10:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.15009","created_at":"2026-07-05T04:10:20Z"},{"alias_kind":"pith_short_12","alias_value":"A6BQ6T7FTDKQ","created_at":"2026-07-05T04:10:20Z"},{"alias_kind":"pith_short_16","alias_value":"A6BQ6T7FTDKQBS4S","created_at":"2026-07-05T04:10:20Z"},{"alias_kind":"pith_short_8","alias_value":"A6BQ6T7F","created_at":"2026-07-05T04:10:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:A6BQ6T7FTDKQBS4S6WARUUT6TP","target":"record","payload":{"canonical_record":{"source":{"id":"2006.15009","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-26T14:30:41Z","cross_cats_sorted":["cs.AI","cs.RO","stat.ML"],"title_canon_sha256":"949a33da0558d24dc89f0b7abf7e9c1d925a4a355678b0fc1e1edaa97f106b0b","abstract_canon_sha256":"8cada46b95507c946c1d2221e8cfb286102b86889f5210b54c14c4b2a640b4a3"},"schema_version":"1.0"},"canonical_sha256":"07830f4fe598d500cb92f5811a527e9bd526ecf262bd7bc98b97cf7a88995ee1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:10:20.828671Z","signature_b64":"NN4W1XibRNq92/248ylMyBx1nrS0GWwVPXxBuTVChoaoB2Voj9RkE1WZyPh4b/JzHoAgM3piUspaKf877PlSAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07830f4fe598d500cb92f5811a527e9bd526ecf262bd7bc98b97cf7a88995ee1","last_reissued_at":"2026-07-05T04:10:20.828262Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:10:20.828262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.15009","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:10:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YCj0980J7cKy4ZxvYi3Riz2gyBBmLaWmZyIb9UDZYx9WF6qgTcKc7CQi/R2qc/3BdHHxscBSJti26s5N5sygAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T09:14:35.643797Z"},"content_sha256":"1d62e347ab6c378df54f97efcfcfc19ca1ded59f6d11e56aa55161643e153189","schema_version":"1.0","event_id":"sha256:1d62e347ab6c378df54f97efcfcfc19ca1ded59f6d11e56aa55161643e153189"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:A6BQ6T7FTDKQBS4S6WARUUT6TP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Unifying Framework for Reinforcement Learning and Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Aske Plaat, Catholijn M. Jonker, Joost Broekens, Thomas M. Moerland","submitted_at":"2020-06-26T14:30:41Z","abstract_excerpt":"Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and planning, which both largely have their own research communities. However, if both research fields solve the same problem, then we might be able to disentangle the common factors in their solution approaches. Therefore, this paper presents a unifying algorithmic framework for reinforcement learning and planning (FRAP), which identifies underlying dimensions on which MDP planning "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.15009","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.15009/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T04:10:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wZcBQrU95bKqtE1jGwnMLs10N+60EaL9OGCYTwhfsmNKNb69WUmmZm86c/sTlNXvjvFDTW8sbw/dvhK/u5orCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T09:14:35.644298Z"},"content_sha256":"0c38d5c6a344b6dc77933d029987124c3e439f001f6e549ac72def80c3cb5da0","schema_version":"1.0","event_id":"sha256:0c38d5c6a344b6dc77933d029987124c3e439f001f6e549ac72def80c3cb5da0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A6BQ6T7FTDKQBS4S6WARUUT6TP/bundle.json","state_url":"https://pith.science/pith/A6BQ6T7FTDKQBS4S6WARUUT6TP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A6BQ6T7FTDKQBS4S6WARUUT6TP/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":"2026-08-11T09:14:35Z","links":{"resolver":"https://pith.science/pith/A6BQ6T7FTDKQBS4S6WARUUT6TP","bundle":"https://pith.science/pith/A6BQ6T7FTDKQBS4S6WARUUT6TP/bundle.json","state":"https://pith.science/pith/A6BQ6T7FTDKQBS4S6WARUUT6TP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A6BQ6T7FTDKQBS4S6WARUUT6TP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:A6BQ6T7FTDKQBS4S6WARUUT6TP","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":"8cada46b95507c946c1d2221e8cfb286102b86889f5210b54c14c4b2a640b4a3","cross_cats_sorted":["cs.AI","cs.RO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-26T14:30:41Z","title_canon_sha256":"949a33da0558d24dc89f0b7abf7e9c1d925a4a355678b0fc1e1edaa97f106b0b"},"schema_version":"1.0","source":{"id":"2006.15009","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.15009","created_at":"2026-07-05T04:10:20Z"},{"alias_kind":"arxiv_version","alias_value":"2006.15009v4","created_at":"2026-07-05T04:10:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.15009","created_at":"2026-07-05T04:10:20Z"},{"alias_kind":"pith_short_12","alias_value":"A6BQ6T7FTDKQ","created_at":"2026-07-05T04:10:20Z"},{"alias_kind":"pith_short_16","alias_value":"A6BQ6T7FTDKQBS4S","created_at":"2026-07-05T04:10:20Z"},{"alias_kind":"pith_short_8","alias_value":"A6BQ6T7F","created_at":"2026-07-05T04:10:20Z"}],"graph_snapshots":[{"event_id":"sha256:0c38d5c6a344b6dc77933d029987124c3e439f001f6e549ac72def80c3cb5da0","target":"graph","created_at":"2026-07-05T04:10:20Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2006.15009/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and planning, which both largely have their own research communities. However, if both research fields solve the same problem, then we might be able to disentangle the common factors in their solution approaches. Therefore, this paper presents a unifying algorithmic framework for reinforcement learning and planning (FRAP), which identifies underlying dimensions on which MDP planning ","authors_text":"Aske Plaat, Catholijn M. Jonker, Joost Broekens, Thomas M. Moerland","cross_cats":["cs.AI","cs.RO","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-26T14:30:41Z","title":"A Unifying Framework for Reinforcement Learning and Planning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.15009","kind":"arxiv","version":4},"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":"sha256:1d62e347ab6c378df54f97efcfcfc19ca1ded59f6d11e56aa55161643e153189","target":"record","created_at":"2026-07-05T04:10:20Z","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":"8cada46b95507c946c1d2221e8cfb286102b86889f5210b54c14c4b2a640b4a3","cross_cats_sorted":["cs.AI","cs.RO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-26T14:30:41Z","title_canon_sha256":"949a33da0558d24dc89f0b7abf7e9c1d925a4a355678b0fc1e1edaa97f106b0b"},"schema_version":"1.0","source":{"id":"2006.15009","kind":"arxiv","version":4}},"canonical_sha256":"07830f4fe598d500cb92f5811a527e9bd526ecf262bd7bc98b97cf7a88995ee1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"07830f4fe598d500cb92f5811a527e9bd526ecf262bd7bc98b97cf7a88995ee1","first_computed_at":"2026-07-05T04:10:20.828262Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:10:20.828262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NN4W1XibRNq92/248ylMyBx1nrS0GWwVPXxBuTVChoaoB2Voj9RkE1WZyPh4b/JzHoAgM3piUspaKf877PlSAA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:10:20.828671Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.15009","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1d62e347ab6c378df54f97efcfcfc19ca1ded59f6d11e56aa55161643e153189","sha256:0c38d5c6a344b6dc77933d029987124c3e439f001f6e549ac72def80c3cb5da0"],"state_sha256":"736a3aaf3ffd275cd01849a9aa712edcbcc8cb546d2b7cdb3237b1e2f892b9f6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ve2vAIjKhEYgbp9mnxr44uArieFMXDIh7rr10RMnI/lh0ZxOsqPywH9VZUfK6CWZ7VMdAtK5IS9JOuaa6O+FDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T09:14:35.648784Z","bundle_sha256":"11bec97d12d5d7e4abbf275fa3359062b2730efc3fd268c6bfc5ea58766fa8fe"}}