{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:CDLA3MQOO6R6XNRV57W5TTCUL5","short_pith_number":"pith:CDLA3MQO","canonical_record":{"source":{"id":"2109.11978","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-09-24T14:04:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5ac3985e7fcb5d168ffa7ff51666870577b1185d15cee2effa0ecfee38462732","abstract_canon_sha256":"d59545b12bf8ccff2fa98281af110b5da5c58c42284317a94baa4fb0b11199d3"},"schema_version":"1.0"},"canonical_sha256":"10d60db20e77a3ebb635efedd9cc545f6e759a583aea825996d6312cdfbd2233","source":{"kind":"arxiv","id":"2109.11978","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.11978","created_at":"2026-07-05T04:49:42Z"},{"alias_kind":"arxiv_version","alias_value":"2109.11978v3","created_at":"2026-07-05T04:49:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.11978","created_at":"2026-07-05T04:49:42Z"},{"alias_kind":"pith_short_12","alias_value":"CDLA3MQOO6R6","created_at":"2026-07-05T04:49:42Z"},{"alias_kind":"pith_short_16","alias_value":"CDLA3MQOO6R6XNRV","created_at":"2026-07-05T04:49:42Z"},{"alias_kind":"pith_short_8","alias_value":"CDLA3MQO","created_at":"2026-07-05T04:49:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:CDLA3MQOO6R6XNRV57W5TTCUL5","target":"record","payload":{"canonical_record":{"source":{"id":"2109.11978","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-09-24T14:04:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5ac3985e7fcb5d168ffa7ff51666870577b1185d15cee2effa0ecfee38462732","abstract_canon_sha256":"d59545b12bf8ccff2fa98281af110b5da5c58c42284317a94baa4fb0b11199d3"},"schema_version":"1.0"},"canonical_sha256":"10d60db20e77a3ebb635efedd9cc545f6e759a583aea825996d6312cdfbd2233","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:49:42.563612Z","signature_b64":"tklbu+/qmOqcAH74/STPahF1q36l+Sh/ZPweQ/U2mD6vN8jBVeTrXxxUGxraD2xLVM8W+uWXR6pcbWJ6PmDgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10d60db20e77a3ebb635efedd9cc545f6e759a583aea825996d6312cdfbd2233","last_reissued_at":"2026-07-05T04:49:42.563142Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:49:42.563142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.11978","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:49:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kusHx0c3bM3aoHjmhnmeralf46G3llYH83Yn+XoNYxAghNtze3N62xQBKna/GvEwPEUIigQ2sKolgSeDYiTPDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:49:54.053993Z"},"content_sha256":"7ce665d791f6ac907355168518a91ca656ab3941661fd72e86c4957270e071de","schema_version":"1.0","event_id":"sha256:7ce665d791f6ac907355168518a91ca656ab3941661fd72e86c4957270e071de"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:CDLA3MQOO6R6XNRV57W5TTCUL5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"David Hoeller, Marco Hutter, Nikita Rudin, Philipp Reist","submitted_at":"2021-09-24T14:04:19Z","abstract_excerpt":"In this work, we present and study a training set-up that achieves fast policy generation for real-world robotic tasks by using massive parallelism on a single workstation GPU. We analyze and discuss the impact of different training algorithm components in the massively parallel regime on the final policy performance and training times. In addition, we present a novel game-inspired curriculum that is well suited for training with thousands of simulated robots in parallel. We evaluate the approach by training the quadrupedal robot ANYmal to walk on challenging terrain. The parallel approach all"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.11978","kind":"arxiv","version":3},"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/2109.11978/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":"2026-07-05T04:49:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AIZFC8+wskkpVXrD1EFJNmgSMKeTB5OklWiFPnhGBpQrBTHLIMg9H8jZJLYVXbwjFcPN/sbMdjkT8KqE5zuECg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:49:54.054570Z"},"content_sha256":"85d60742e753e920e0b644563a6738bbd3b23a72344d9e1288a9b349d74880a0","schema_version":"1.0","event_id":"sha256:85d60742e753e920e0b644563a6738bbd3b23a72344d9e1288a9b349d74880a0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CDLA3MQOO6R6XNRV57W5TTCUL5/bundle.json","state_url":"https://pith.science/pith/CDLA3MQOO6R6XNRV57W5TTCUL5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CDLA3MQOO6R6XNRV57W5TTCUL5/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":"2026-08-03T16:49:54Z","links":{"resolver":"https://pith.science/pith/CDLA3MQOO6R6XNRV57W5TTCUL5","bundle":"https://pith.science/pith/CDLA3MQOO6R6XNRV57W5TTCUL5/bundle.json","state":"https://pith.science/pith/CDLA3MQOO6R6XNRV57W5TTCUL5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CDLA3MQOO6R6XNRV57W5TTCUL5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:CDLA3MQOO6R6XNRV57W5TTCUL5","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":"d59545b12bf8ccff2fa98281af110b5da5c58c42284317a94baa4fb0b11199d3","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-09-24T14:04:19Z","title_canon_sha256":"5ac3985e7fcb5d168ffa7ff51666870577b1185d15cee2effa0ecfee38462732"},"schema_version":"1.0","source":{"id":"2109.11978","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.11978","created_at":"2026-07-05T04:49:42Z"},{"alias_kind":"arxiv_version","alias_value":"2109.11978v3","created_at":"2026-07-05T04:49:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.11978","created_at":"2026-07-05T04:49:42Z"},{"alias_kind":"pith_short_12","alias_value":"CDLA3MQOO6R6","created_at":"2026-07-05T04:49:42Z"},{"alias_kind":"pith_short_16","alias_value":"CDLA3MQOO6R6XNRV","created_at":"2026-07-05T04:49:42Z"},{"alias_kind":"pith_short_8","alias_value":"CDLA3MQO","created_at":"2026-07-05T04:49:42Z"}],"graph_snapshots":[{"event_id":"sha256:85d60742e753e920e0b644563a6738bbd3b23a72344d9e1288a9b349d74880a0","target":"graph","created_at":"2026-07-05T04:49:42Z","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/2109.11978/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we present and study a training set-up that achieves fast policy generation for real-world robotic tasks by using massive parallelism on a single workstation GPU. We analyze and discuss the impact of different training algorithm components in the massively parallel regime on the final policy performance and training times. In addition, we present a novel game-inspired curriculum that is well suited for training with thousands of simulated robots in parallel. We evaluate the approach by training the quadrupedal robot ANYmal to walk on challenging terrain. The parallel approach all","authors_text":"David Hoeller, Marco Hutter, Nikita Rudin, Philipp Reist","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-09-24T14:04:19Z","title":"Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.11978","kind":"arxiv","version":3},"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":"sha256:7ce665d791f6ac907355168518a91ca656ab3941661fd72e86c4957270e071de","target":"record","created_at":"2026-07-05T04:49:42Z","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":"d59545b12bf8ccff2fa98281af110b5da5c58c42284317a94baa4fb0b11199d3","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-09-24T14:04:19Z","title_canon_sha256":"5ac3985e7fcb5d168ffa7ff51666870577b1185d15cee2effa0ecfee38462732"},"schema_version":"1.0","source":{"id":"2109.11978","kind":"arxiv","version":3}},"canonical_sha256":"10d60db20e77a3ebb635efedd9cc545f6e759a583aea825996d6312cdfbd2233","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"10d60db20e77a3ebb635efedd9cc545f6e759a583aea825996d6312cdfbd2233","first_computed_at":"2026-07-05T04:49:42.563142Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:49:42.563142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tklbu+/qmOqcAH74/STPahF1q36l+Sh/ZPweQ/U2mD6vN8jBVeTrXxxUGxraD2xLVM8W+uWXR6pcbWJ6PmDgDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:49:42.563612Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.11978","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7ce665d791f6ac907355168518a91ca656ab3941661fd72e86c4957270e071de","sha256:85d60742e753e920e0b644563a6738bbd3b23a72344d9e1288a9b349d74880a0"],"state_sha256":"a86c6d7301c652f6992c5804fd2a198c2d201db81821debcca7052874dad91b5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"otOO1dlLbR7Me8qXhOW5EniFkxkSGFS5tZWn2REXeeNiz9uyM0DuLaX1oMGyC9B7+9uInOkSU5WlhN8zgHUqCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T16:49:54.059817Z","bundle_sha256":"04b3bafaca03b264b119042ffbde055065c23f041e83d3c29e71e015bdfa8ed6"}}