{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:PKLDT46YMKUOKYTYVRGKKMVIC4","short_pith_number":"pith:PKLDT46Y","canonical_record":{"source":{"id":"1911.00357","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-01T13:07:37Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"28844b1bdb4aadd0aa53c0593a2c0f954150fa81db6bb0afa98ba99f4336a3a8","abstract_canon_sha256":"9bc8dbea0153d0705d8b228ab2c4a0a7b4eecd628a74de5c621d5d502304de76"},"schema_version":"1.0"},"canonical_sha256":"7a9639f3d862a8e56278ac4ca532a8173c7d0df0320a2c1f82ffb6d8d23080d8","source":{"kind":"arxiv","id":"1911.00357","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.00357","created_at":"2026-07-05T00:34:19Z"},{"alias_kind":"arxiv_version","alias_value":"1911.00357v2","created_at":"2026-07-05T00:34:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.00357","created_at":"2026-07-05T00:34:19Z"},{"alias_kind":"pith_short_12","alias_value":"PKLDT46YMKUO","created_at":"2026-07-05T00:34:19Z"},{"alias_kind":"pith_short_16","alias_value":"PKLDT46YMKUOKYTY","created_at":"2026-07-05T00:34:19Z"},{"alias_kind":"pith_short_8","alias_value":"PKLDT46Y","created_at":"2026-07-05T00:34:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:PKLDT46YMKUOKYTYVRGKKMVIC4","target":"record","payload":{"canonical_record":{"source":{"id":"1911.00357","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-01T13:07:37Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"28844b1bdb4aadd0aa53c0593a2c0f954150fa81db6bb0afa98ba99f4336a3a8","abstract_canon_sha256":"9bc8dbea0153d0705d8b228ab2c4a0a7b4eecd628a74de5c621d5d502304de76"},"schema_version":"1.0"},"canonical_sha256":"7a9639f3d862a8e56278ac4ca532a8173c7d0df0320a2c1f82ffb6d8d23080d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:34:19.057135Z","signature_b64":"BwNL2RUP4DphmAUUmqCl1wMVOm4nZ9+uAM6BURMnfMRF/mEURNc3Oq2VykVEXtCMTYVBdWYJG4if3Bq1wq0WAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a9639f3d862a8e56278ac4ca532a8173c7d0df0320a2c1f82ffb6d8d23080d8","last_reissued_at":"2026-07-05T00:34:19.056642Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:34:19.056642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1911.00357","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:34:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"16gUSvHfwAwT+A59vv/RdcPc59gnJ6GKiJNPJF5FLviMtdb0gqPoSx5aLuIVVRb3/nlwtpMwpa1Uku2ba5P4Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T22:32:56.640450Z"},"content_sha256":"f0c53bbc72905502b1e13768d83fae4ba9f9a891a6e468811148218df3d3ce3c","schema_version":"1.0","event_id":"sha256:f0c53bbc72905502b1e13768d83fae4ba9f9a891a6e468811148218df3d3ce3c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:PKLDT46YMKUOKYTYVRGKKMVIC4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Abhishek Kadian, Ari Morcos, Devi Parikh, Dhruv Batra, Erik Wijmans, Irfan Essa, Manolis Savva, Stefan Lee","submitted_at":"2019-11-01T13:07:37Z","abstract_excerpt":"We present Decentralized Distributed Proximal Policy Optimization (DD-PPO), a method for distributed reinforcement learning in resource-intensive simulated environments. DD-PPO is distributed (uses multiple machines), decentralized (lacks a centralized server), and synchronous (no computation is ever stale), making it conceptually simple and easy to implement. In our experiments on training virtual robots to navigate in Habitat-Sim, DD-PPO exhibits near-linear scaling -- achieving a speedup of 107x on 128 GPUs over a serial implementation. We leverage this scaling to train an agent for 2.5 Bil"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.00357","kind":"arxiv","version":2},"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/1911.00357/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":"2026-07-05T00:34:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ulWaS3we4pZLE5a2r6oXRe/jZY80M3qos0wawh1j49XJ6FyGXBpxpEUBaundCLkl+ayazFLbqd0NrrThY3H9CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T22:32:56.640979Z"},"content_sha256":"1e398ecacd66e53b04cb39cddd36d7f412c998b507e19f86fd88826bc4bade96","schema_version":"1.0","event_id":"sha256:1e398ecacd66e53b04cb39cddd36d7f412c998b507e19f86fd88826bc4bade96"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PKLDT46YMKUOKYTYVRGKKMVIC4/bundle.json","state_url":"https://pith.science/pith/PKLDT46YMKUOKYTYVRGKKMVIC4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PKLDT46YMKUOKYTYVRGKKMVIC4/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":"2026-08-05T22:32:56Z","links":{"resolver":"https://pith.science/pith/PKLDT46YMKUOKYTYVRGKKMVIC4","bundle":"https://pith.science/pith/PKLDT46YMKUOKYTYVRGKKMVIC4/bundle.json","state":"https://pith.science/pith/PKLDT46YMKUOKYTYVRGKKMVIC4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PKLDT46YMKUOKYTYVRGKKMVIC4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:PKLDT46YMKUOKYTYVRGKKMVIC4","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":"9bc8dbea0153d0705d8b228ab2c4a0a7b4eecd628a74de5c621d5d502304de76","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-01T13:07:37Z","title_canon_sha256":"28844b1bdb4aadd0aa53c0593a2c0f954150fa81db6bb0afa98ba99f4336a3a8"},"schema_version":"1.0","source":{"id":"1911.00357","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.00357","created_at":"2026-07-05T00:34:19Z"},{"alias_kind":"arxiv_version","alias_value":"1911.00357v2","created_at":"2026-07-05T00:34:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.00357","created_at":"2026-07-05T00:34:19Z"},{"alias_kind":"pith_short_12","alias_value":"PKLDT46YMKUO","created_at":"2026-07-05T00:34:19Z"},{"alias_kind":"pith_short_16","alias_value":"PKLDT46YMKUOKYTY","created_at":"2026-07-05T00:34:19Z"},{"alias_kind":"pith_short_8","alias_value":"PKLDT46Y","created_at":"2026-07-05T00:34:19Z"}],"graph_snapshots":[{"event_id":"sha256:1e398ecacd66e53b04cb39cddd36d7f412c998b507e19f86fd88826bc4bade96","target":"graph","created_at":"2026-07-05T00:34:19Z","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/1911.00357/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present Decentralized Distributed Proximal Policy Optimization (DD-PPO), a method for distributed reinforcement learning in resource-intensive simulated environments. DD-PPO is distributed (uses multiple machines), decentralized (lacks a centralized server), and synchronous (no computation is ever stale), making it conceptually simple and easy to implement. In our experiments on training virtual robots to navigate in Habitat-Sim, DD-PPO exhibits near-linear scaling -- achieving a speedup of 107x on 128 GPUs over a serial implementation. We leverage this scaling to train an agent for 2.5 Bil","authors_text":"Abhishek Kadian, Ari Morcos, Devi Parikh, Dhruv Batra, Erik Wijmans, Irfan Essa, Manolis Savva, Stefan Lee","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-01T13:07:37Z","title":"DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.00357","kind":"arxiv","version":2},"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:f0c53bbc72905502b1e13768d83fae4ba9f9a891a6e468811148218df3d3ce3c","target":"record","created_at":"2026-07-05T00:34:19Z","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":"9bc8dbea0153d0705d8b228ab2c4a0a7b4eecd628a74de5c621d5d502304de76","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-01T13:07:37Z","title_canon_sha256":"28844b1bdb4aadd0aa53c0593a2c0f954150fa81db6bb0afa98ba99f4336a3a8"},"schema_version":"1.0","source":{"id":"1911.00357","kind":"arxiv","version":2}},"canonical_sha256":"7a9639f3d862a8e56278ac4ca532a8173c7d0df0320a2c1f82ffb6d8d23080d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7a9639f3d862a8e56278ac4ca532a8173c7d0df0320a2c1f82ffb6d8d23080d8","first_computed_at":"2026-07-05T00:34:19.056642Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:34:19.056642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BwNL2RUP4DphmAUUmqCl1wMVOm4nZ9+uAM6BURMnfMRF/mEURNc3Oq2VykVEXtCMTYVBdWYJG4if3Bq1wq0WAg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:34:19.057135Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.00357","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0c53bbc72905502b1e13768d83fae4ba9f9a891a6e468811148218df3d3ce3c","sha256:1e398ecacd66e53b04cb39cddd36d7f412c998b507e19f86fd88826bc4bade96"],"state_sha256":"0542294d9ff4905d7b463c2deaab7eb2ba55423ca67c8807588eda9853bd922e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MtmPINW2BJeJT51bKW7xKluRN8hgkXESmle+Kzq3VFm3TtlGrP2ExowXHkw7rAIi6oHuCq6YkEjwj5W8Y+/UAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T22:32:56.645901Z","bundle_sha256":"ba97d36ac5729920cef855163a84e3492db022c83d526f87bdf3bb93932d53ba"}}