{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:IEJNU326MCSSCF6O74JC5JJJYK","short_pith_number":"pith:IEJNU326","canonical_record":{"source":{"id":"2311.06673","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-11T22:05:10Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"94483fbedb2acf836704453421ff5a2203ae4ec996ee5349916b38cfa694b0ef","abstract_canon_sha256":"83150f34990d6484725815051db20d85f1e9f3b8a97808f826c7fb527a76158a"},"schema_version":"1.0"},"canonical_sha256":"4112da6f5e60a52117ceff122ea529c2877f2d05679aa7ecdaa5283d4d32d45c","source":{"kind":"arxiv","id":"2311.06673","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.06673","created_at":"2026-07-05T07:11:43Z"},{"alias_kind":"arxiv_version","alias_value":"2311.06673v1","created_at":"2026-07-05T07:11:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.06673","created_at":"2026-07-05T07:11:43Z"},{"alias_kind":"pith_short_12","alias_value":"IEJNU326MCSS","created_at":"2026-07-05T07:11:43Z"},{"alias_kind":"pith_short_16","alias_value":"IEJNU326MCSSCF6O","created_at":"2026-07-05T07:11:43Z"},{"alias_kind":"pith_short_8","alias_value":"IEJNU326","created_at":"2026-07-05T07:11:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:IEJNU326MCSSCF6O74JC5JJJYK","target":"record","payload":{"canonical_record":{"source":{"id":"2311.06673","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-11T22:05:10Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"94483fbedb2acf836704453421ff5a2203ae4ec996ee5349916b38cfa694b0ef","abstract_canon_sha256":"83150f34990d6484725815051db20d85f1e9f3b8a97808f826c7fb527a76158a"},"schema_version":"1.0"},"canonical_sha256":"4112da6f5e60a52117ceff122ea529c2877f2d05679aa7ecdaa5283d4d32d45c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:11:43.641364Z","signature_b64":"0pXNUEM/xfOclhsFIGEgxwWfidO65YCzbWlX+P6CdeyAJ2Ltv0lfv1V0zYnR/mWdn+vZzwuyamJXRKK+OZOIBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4112da6f5e60a52117ceff122ea529c2877f2d05679aa7ecdaa5283d4d32d45c","last_reissued_at":"2026-07-05T07:11:43.640831Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:11:43.640831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.06673","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":"2026-07-05T07:11:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RBpzlj5iPexft53NXnxZr4ou5hqsWm8+1kxlW8Z2uCTW5vK3Oo7MwpeuZRPu8d5SqCIuInUHkpggjNzj66VbCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T01:13:02.792970Z"},"content_sha256":"b24315a4f4172bd6cdfcea45061bad2a8e505ec028452c54f776428e7a3d3f8f","schema_version":"1.0","event_id":"sha256:b24315a4f4172bd6cdfcea45061bad2a8e505ec028452c54f776428e7a3d3f8f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:IEJNU326MCSSCF6O74JC5JJJYK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.LG","authors_text":"H. Eric Tseng, Huei Peng, Lu Wen, Songan Zhang","submitted_at":"2023-11-11T22:05:10Z","abstract_excerpt":"Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks. However, most state-of-the-art algorithms require the meta-training tasks to have a dense coverage on the task distribution and a great amount of data for each of them. In this paper, we propose MetaDreamer, a context-based Meta RL algorithm that requires less real training tasks and data by doing meta-imagination and MDP-imagination. We perform meta-imagination by interpolating on the learned latent context space with disentangled prope"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.06673","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2311.06673/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":"2026-07-05T07:11:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zSFwV9wghUY0r6K2riW0XBigkyxsnXhq0FDW7OvbjYTobeN+zvaaMy0Ibik4R2e1O/9E4PmSNEOPwgjKAL+KBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T01:13:02.793925Z"},"content_sha256":"cb70369b7f5c4a8a0a6115b4482fc29e0b10085ebe6fe4e3f3401ebc457ca4e4","schema_version":"1.0","event_id":"sha256:cb70369b7f5c4a8a0a6115b4482fc29e0b10085ebe6fe4e3f3401ebc457ca4e4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IEJNU326MCSSCF6O74JC5JJJYK/bundle.json","state_url":"https://pith.science/pith/IEJNU326MCSSCF6O74JC5JJJYK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IEJNU326MCSSCF6O74JC5JJJYK/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":"2026-08-11T01:13:02Z","links":{"resolver":"https://pith.science/pith/IEJNU326MCSSCF6O74JC5JJJYK","bundle":"https://pith.science/pith/IEJNU326MCSSCF6O74JC5JJJYK/bundle.json","state":"https://pith.science/pith/IEJNU326MCSSCF6O74JC5JJJYK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IEJNU326MCSSCF6O74JC5JJJYK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:IEJNU326MCSSCF6O74JC5JJJYK","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":"83150f34990d6484725815051db20d85f1e9f3b8a97808f826c7fb527a76158a","cross_cats_sorted":["cs.AI","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-11T22:05:10Z","title_canon_sha256":"94483fbedb2acf836704453421ff5a2203ae4ec996ee5349916b38cfa694b0ef"},"schema_version":"1.0","source":{"id":"2311.06673","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.06673","created_at":"2026-07-05T07:11:43Z"},{"alias_kind":"arxiv_version","alias_value":"2311.06673v1","created_at":"2026-07-05T07:11:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.06673","created_at":"2026-07-05T07:11:43Z"},{"alias_kind":"pith_short_12","alias_value":"IEJNU326MCSS","created_at":"2026-07-05T07:11:43Z"},{"alias_kind":"pith_short_16","alias_value":"IEJNU326MCSSCF6O","created_at":"2026-07-05T07:11:43Z"},{"alias_kind":"pith_short_8","alias_value":"IEJNU326","created_at":"2026-07-05T07:11:43Z"}],"graph_snapshots":[{"event_id":"sha256:cb70369b7f5c4a8a0a6115b4482fc29e0b10085ebe6fe4e3f3401ebc457ca4e4","target":"graph","created_at":"2026-07-05T07:11:43Z","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/2311.06673/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks. However, most state-of-the-art algorithms require the meta-training tasks to have a dense coverage on the task distribution and a great amount of data for each of them. In this paper, we propose MetaDreamer, a context-based Meta RL algorithm that requires less real training tasks and data by doing meta-imagination and MDP-imagination. We perform meta-imagination by interpolating on the learned latent context space with disentangled prope","authors_text":"H. Eric Tseng, Huei Peng, Lu Wen, Songan Zhang","cross_cats":["cs.AI","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-11T22:05:10Z","title":"Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.06673","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:b24315a4f4172bd6cdfcea45061bad2a8e505ec028452c54f776428e7a3d3f8f","target":"record","created_at":"2026-07-05T07:11:43Z","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":"83150f34990d6484725815051db20d85f1e9f3b8a97808f826c7fb527a76158a","cross_cats_sorted":["cs.AI","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-11T22:05:10Z","title_canon_sha256":"94483fbedb2acf836704453421ff5a2203ae4ec996ee5349916b38cfa694b0ef"},"schema_version":"1.0","source":{"id":"2311.06673","kind":"arxiv","version":1}},"canonical_sha256":"4112da6f5e60a52117ceff122ea529c2877f2d05679aa7ecdaa5283d4d32d45c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4112da6f5e60a52117ceff122ea529c2877f2d05679aa7ecdaa5283d4d32d45c","first_computed_at":"2026-07-05T07:11:43.640831Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:11:43.640831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0pXNUEM/xfOclhsFIGEgxwWfidO65YCzbWlX+P6CdeyAJ2Ltv0lfv1V0zYnR/mWdn+vZzwuyamJXRKK+OZOIBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:11:43.641364Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.06673","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b24315a4f4172bd6cdfcea45061bad2a8e505ec028452c54f776428e7a3d3f8f","sha256:cb70369b7f5c4a8a0a6115b4482fc29e0b10085ebe6fe4e3f3401ebc457ca4e4"],"state_sha256":"37d713a80134b7b864fa5f080b648d1fc5e0d487bd80a768b5ada527a5153760"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cpn7VWfyRNLWgaPvjjFa7AOfv9kbO2tUH/h44caQhUWAVl8KkJeeWrUhBr+RV4NRyIZ1mAIo4rTZYWJ+S8EaDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T01:13:02.801717Z","bundle_sha256":"a976e9966795d1b47c9db52d88348df46c1726c8d7429d8e098ab191f6dbb7f3"}}