{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:4T5ZGHK6HWORYQ746GZ42YTI52","short_pith_number":"pith:4T5ZGHK6","canonical_record":{"source":{"id":"1909.01646","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T09:30:55Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"3bb8ffe75675e159eed76dfebfc44a8b42a07dd976c29748eeed0dbd0e91a4dc","abstract_canon_sha256":"d9531b5de8cd8c4b1d5856ae9fac6b20307f5e10b74a6db425ecec26fecadafa"},"schema_version":"1.0"},"canonical_sha256":"e4fb931d5e3d9d1c43fcf1b3cd6268eeb83c4bf86b1e10c8105a9e595da7d802","source":{"kind":"arxiv","id":"1909.01646","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.01646","created_at":"2026-07-05T00:02:18Z"},{"alias_kind":"arxiv_version","alias_value":"1909.01646v1","created_at":"2026-07-05T00:02:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.01646","created_at":"2026-07-05T00:02:18Z"},{"alias_kind":"pith_short_12","alias_value":"4T5ZGHK6HWOR","created_at":"2026-07-05T00:02:18Z"},{"alias_kind":"pith_short_16","alias_value":"4T5ZGHK6HWORYQ74","created_at":"2026-07-05T00:02:18Z"},{"alias_kind":"pith_short_8","alias_value":"4T5ZGHK6","created_at":"2026-07-05T00:02:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:4T5ZGHK6HWORYQ746GZ42YTI52","target":"record","payload":{"canonical_record":{"source":{"id":"1909.01646","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T09:30:55Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"3bb8ffe75675e159eed76dfebfc44a8b42a07dd976c29748eeed0dbd0e91a4dc","abstract_canon_sha256":"d9531b5de8cd8c4b1d5856ae9fac6b20307f5e10b74a6db425ecec26fecadafa"},"schema_version":"1.0"},"canonical_sha256":"e4fb931d5e3d9d1c43fcf1b3cd6268eeb83c4bf86b1e10c8105a9e595da7d802","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:02:18.556915Z","signature_b64":"zUaSzoZOHMiRrsTlvH4rTGCY4uTpgpjHiQYbxQUcYcU7Paz+VcNQLMllqtm8cs4+liAe79SOAacG1TBE2//vCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4fb931d5e3d9d1c43fcf1b3cd6268eeb83c4bf86b1e10c8105a9e595da7d802","last_reissued_at":"2026-07-05T00:02:18.556504Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:02:18.556504Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.01646","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":"2026-07-05T00:02:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2MQfrCkIss9bZBvNoZ5jbDs95/qkTysK6f2N3rCB2EAJYDApOLPeyIfim1Jcj2ogT+s3Wax+pkhYHarYT0tbCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T03:04:27.554186Z"},"content_sha256":"eefdeca902882176aa9a3591b93547c6839581ef04b239ea7899f72c31c0ea61","schema_version":"1.0","event_id":"sha256:eefdeca902882176aa9a3591b93547c6839581ef04b239ea7899f72c31c0ea61"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:4T5ZGHK6HWORYQ746GZ42YTI52","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Leonard Adolphs, Thomas Hofmann","submitted_at":"2019-09-04T09:30:55Z","abstract_excerpt":"While Reinforcement Learning (RL) approaches lead to significant achievements in a variety of areas in recent history, natural language tasks remained mostly unaffected, due to the compositional and combinatorial nature that makes them notoriously hard to optimize. With the emerging field of Text-Based Games (TBGs), researchers try to bridge this gap. Inspired by the success of RL algorithms on Atari games, the idea is to develop new methods in a restricted game world and then gradually move to more complex environments. Previous work in the area of TBGs has mainly focused on solving individua"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.01646","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/1909.01646/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":"2026-07-05T00:02:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4JXMk7rSi1frwPVkIhPx/alXZvriAlS6XhuRPIG7+kneEBcr8O4HNigfcPR5uTbS8024McTavuhLCffleLMADg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T03:04:27.554699Z"},"content_sha256":"2e5d7d5a49e8ec172e81d8c86f85372f65e74d54c891acf65fec9a9240eae267","schema_version":"1.0","event_id":"sha256:2e5d7d5a49e8ec172e81d8c86f85372f65e74d54c891acf65fec9a9240eae267"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4T5ZGHK6HWORYQ746GZ42YTI52/bundle.json","state_url":"https://pith.science/pith/4T5ZGHK6HWORYQ746GZ42YTI52/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4T5ZGHK6HWORYQ746GZ42YTI52/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":"2026-08-16T03:04:27Z","links":{"resolver":"https://pith.science/pith/4T5ZGHK6HWORYQ746GZ42YTI52","bundle":"https://pith.science/pith/4T5ZGHK6HWORYQ746GZ42YTI52/bundle.json","state":"https://pith.science/pith/4T5ZGHK6HWORYQ746GZ42YTI52/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4T5ZGHK6HWORYQ746GZ42YTI52/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:4T5ZGHK6HWORYQ746GZ42YTI52","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":"d9531b5de8cd8c4b1d5856ae9fac6b20307f5e10b74a6db425ecec26fecadafa","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T09:30:55Z","title_canon_sha256":"3bb8ffe75675e159eed76dfebfc44a8b42a07dd976c29748eeed0dbd0e91a4dc"},"schema_version":"1.0","source":{"id":"1909.01646","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.01646","created_at":"2026-07-05T00:02:18Z"},{"alias_kind":"arxiv_version","alias_value":"1909.01646v1","created_at":"2026-07-05T00:02:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.01646","created_at":"2026-07-05T00:02:18Z"},{"alias_kind":"pith_short_12","alias_value":"4T5ZGHK6HWOR","created_at":"2026-07-05T00:02:18Z"},{"alias_kind":"pith_short_16","alias_value":"4T5ZGHK6HWORYQ74","created_at":"2026-07-05T00:02:18Z"},{"alias_kind":"pith_short_8","alias_value":"4T5ZGHK6","created_at":"2026-07-05T00:02:18Z"}],"graph_snapshots":[{"event_id":"sha256:2e5d7d5a49e8ec172e81d8c86f85372f65e74d54c891acf65fec9a9240eae267","target":"graph","created_at":"2026-07-05T00:02:18Z","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/1909.01646/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While Reinforcement Learning (RL) approaches lead to significant achievements in a variety of areas in recent history, natural language tasks remained mostly unaffected, due to the compositional and combinatorial nature that makes them notoriously hard to optimize. With the emerging field of Text-Based Games (TBGs), researchers try to bridge this gap. Inspired by the success of RL algorithms on Atari games, the idea is to develop new methods in a restricted game world and then gradually move to more complex environments. Previous work in the area of TBGs has mainly focused on solving individua","authors_text":"Leonard Adolphs, Thomas Hofmann","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T09:30:55Z","title":"LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.01646","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:eefdeca902882176aa9a3591b93547c6839581ef04b239ea7899f72c31c0ea61","target":"record","created_at":"2026-07-05T00:02:18Z","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":"d9531b5de8cd8c4b1d5856ae9fac6b20307f5e10b74a6db425ecec26fecadafa","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T09:30:55Z","title_canon_sha256":"3bb8ffe75675e159eed76dfebfc44a8b42a07dd976c29748eeed0dbd0e91a4dc"},"schema_version":"1.0","source":{"id":"1909.01646","kind":"arxiv","version":1}},"canonical_sha256":"e4fb931d5e3d9d1c43fcf1b3cd6268eeb83c4bf86b1e10c8105a9e595da7d802","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e4fb931d5e3d9d1c43fcf1b3cd6268eeb83c4bf86b1e10c8105a9e595da7d802","first_computed_at":"2026-07-05T00:02:18.556504Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:02:18.556504Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zUaSzoZOHMiRrsTlvH4rTGCY4uTpgpjHiQYbxQUcYcU7Paz+VcNQLMllqtm8cs4+liAe79SOAacG1TBE2//vCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:02:18.556915Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.01646","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eefdeca902882176aa9a3591b93547c6839581ef04b239ea7899f72c31c0ea61","sha256:2e5d7d5a49e8ec172e81d8c86f85372f65e74d54c891acf65fec9a9240eae267"],"state_sha256":"5f64db1b7d64bc960734bfbe81d9d2f4bd660bf0db4dfec6b4960d7476c1380b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wvmRblrn01kt9AQfUh53Z+XDMJFhctsyVxBXKSSQ6w+uN8wyx7Nu9Oxq12uopABgeIL4vpVPQ1wzu2y9z4DEDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T03:04:27.559816Z","bundle_sha256":"e097795e251539cbf608e18cf0ba04c7b7bff57d4675cce678907493cd6eef0b"}}