{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:3GHD4LC7V2JG5S625QGKPQA5RS","short_pith_number":"pith:3GHD4LC7","canonical_record":{"source":{"id":"1901.08129","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-01-23T21:01:27Z","cross_cats_sorted":[],"title_canon_sha256":"f4aa9d1f5e5b62353cad2f940ede6196a342dd271c4c4dd1769cd6b827beb036","abstract_canon_sha256":"de6df5111ecd4a7c70065174278bd6948cb1e699e2d575cbe27fc7bb8507c658"},"schema_version":"1.0"},"canonical_sha256":"d98e3e2c5fae926ecbdaec0ca7c01d8ca97bb6ea14e88722654154994deb725a","source":{"kind":"arxiv","id":"1901.08129","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.08129","created_at":"2026-07-05T10:47:22Z"},{"alias_kind":"arxiv_version","alias_value":"1901.08129v2","created_at":"2026-07-05T10:47:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.08129","created_at":"2026-07-05T10:47:22Z"},{"alias_kind":"pith_short_12","alias_value":"3GHD4LC7V2JG","created_at":"2026-07-05T10:47:22Z"},{"alias_kind":"pith_short_16","alias_value":"3GHD4LC7V2JG5S62","created_at":"2026-07-05T10:47:22Z"},{"alias_kind":"pith_short_8","alias_value":"3GHD4LC7","created_at":"2026-07-05T10:47:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:3GHD4LC7V2JG5S625QGKPQA5RS","target":"record","payload":{"canonical_record":{"source":{"id":"1901.08129","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-01-23T21:01:27Z","cross_cats_sorted":[],"title_canon_sha256":"f4aa9d1f5e5b62353cad2f940ede6196a342dd271c4c4dd1769cd6b827beb036","abstract_canon_sha256":"de6df5111ecd4a7c70065174278bd6948cb1e699e2d575cbe27fc7bb8507c658"},"schema_version":"1.0"},"canonical_sha256":"d98e3e2c5fae926ecbdaec0ca7c01d8ca97bb6ea14e88722654154994deb725a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:47:22.864392Z","signature_b64":"RrEcoobOtE8r4/U/rDEQKAxrEn1qQFB9YMlIWD/85ecB4NMydyvHWxw22PpnFJvCRNR/+vhSQLifEcJCnJJ5Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d98e3e2c5fae926ecbdaec0ca7c01d8ca97bb6ea14e88722654154994deb725a","last_reissued_at":"2026-07-05T10:47:22.863920Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:47:22.863920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.08129","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":"2026-07-05T10:47:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lqITcZ4KfK/7zJ4JlsP8X1MfALhhnOXqfBYl13QSMzFZeou11W2R+dQ0DIAqbohGrfoNKuNUK2Cr7DKGKcavBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:26:01.213271Z"},"content_sha256":"b0045e13b1ff8d632869e42508f4662f22ac7417439d9cad90634143d1769e76","schema_version":"1.0","event_id":"sha256:b0045e13b1ff8d632869e42508f4662f22ac7417439d9cad90634143d1769e76"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:3GHD4LC7V2JG5S625QGKPQA5RS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Multi-Agent Reinforcement Learning in Malm\\\"O (MARL\\\"O) Competition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Andre Kramer, Daniel Ionita, Diego Perez-Liebana, Katja Hofmann, Noboru Kuno, Raluca D. Gaina, Sam Devlin, Sharada Prasanna Mohanty","submitted_at":"2019-01-23T21:01:27Z","abstract_excerpt":"Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types. The Multi-Agent Reinforcement Learning in Malm\\\"O (MARL\\\"O) competition is a new challenge that proposes research in this domain using multiple 3D games. The goal of this contest is to foster research in general agents that can learn across different games and opponent types, proposing a challenge as a milestone in the direction of Artificial General Intelligence."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.08129","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/1901.08129/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":"2026-07-05T10:47:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ui2XwOv9zkRFfsmEpLj6r1B/DqprdqDlARCs3Thgc6dyvYIGA5ey6HLXqdNqXipb8tRqgW8ugwYWJRn5/QydAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T06:26:01.214200Z"},"content_sha256":"f51c6b457fa281a7eb1e972d3d62edf4f29d85c6d6f1251eb1598918e532f3af","schema_version":"1.0","event_id":"sha256:f51c6b457fa281a7eb1e972d3d62edf4f29d85c6d6f1251eb1598918e532f3af"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3GHD4LC7V2JG5S625QGKPQA5RS/bundle.json","state_url":"https://pith.science/pith/3GHD4LC7V2JG5S625QGKPQA5RS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3GHD4LC7V2JG5S625QGKPQA5RS/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":"2026-08-06T06:26:01Z","links":{"resolver":"https://pith.science/pith/3GHD4LC7V2JG5S625QGKPQA5RS","bundle":"https://pith.science/pith/3GHD4LC7V2JG5S625QGKPQA5RS/bundle.json","state":"https://pith.science/pith/3GHD4LC7V2JG5S625QGKPQA5RS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3GHD4LC7V2JG5S625QGKPQA5RS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:3GHD4LC7V2JG5S625QGKPQA5RS","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":"de6df5111ecd4a7c70065174278bd6948cb1e699e2d575cbe27fc7bb8507c658","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-01-23T21:01:27Z","title_canon_sha256":"f4aa9d1f5e5b62353cad2f940ede6196a342dd271c4c4dd1769cd6b827beb036"},"schema_version":"1.0","source":{"id":"1901.08129","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.08129","created_at":"2026-07-05T10:47:22Z"},{"alias_kind":"arxiv_version","alias_value":"1901.08129v2","created_at":"2026-07-05T10:47:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.08129","created_at":"2026-07-05T10:47:22Z"},{"alias_kind":"pith_short_12","alias_value":"3GHD4LC7V2JG","created_at":"2026-07-05T10:47:22Z"},{"alias_kind":"pith_short_16","alias_value":"3GHD4LC7V2JG5S62","created_at":"2026-07-05T10:47:22Z"},{"alias_kind":"pith_short_8","alias_value":"3GHD4LC7","created_at":"2026-07-05T10:47:22Z"}],"graph_snapshots":[{"event_id":"sha256:f51c6b457fa281a7eb1e972d3d62edf4f29d85c6d6f1251eb1598918e532f3af","target":"graph","created_at":"2026-07-05T10:47:22Z","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/1901.08129/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types. The Multi-Agent Reinforcement Learning in Malm\\\"O (MARL\\\"O) competition is a new challenge that proposes research in this domain using multiple 3D games. The goal of this contest is to foster research in general agents that can learn across different games and opponent types, proposing a challenge as a milestone in the direction of Artificial General Intelligence.","authors_text":"Andre Kramer, Daniel Ionita, Diego Perez-Liebana, Katja Hofmann, Noboru Kuno, Raluca D. Gaina, Sam Devlin, Sharada Prasanna Mohanty","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-01-23T21:01:27Z","title":"The Multi-Agent Reinforcement Learning in Malm\\\"O (MARL\\\"O) Competition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.08129","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:b0045e13b1ff8d632869e42508f4662f22ac7417439d9cad90634143d1769e76","target":"record","created_at":"2026-07-05T10:47:22Z","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":"de6df5111ecd4a7c70065174278bd6948cb1e699e2d575cbe27fc7bb8507c658","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-01-23T21:01:27Z","title_canon_sha256":"f4aa9d1f5e5b62353cad2f940ede6196a342dd271c4c4dd1769cd6b827beb036"},"schema_version":"1.0","source":{"id":"1901.08129","kind":"arxiv","version":2}},"canonical_sha256":"d98e3e2c5fae926ecbdaec0ca7c01d8ca97bb6ea14e88722654154994deb725a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d98e3e2c5fae926ecbdaec0ca7c01d8ca97bb6ea14e88722654154994deb725a","first_computed_at":"2026-07-05T10:47:22.863920Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:47:22.863920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RrEcoobOtE8r4/U/rDEQKAxrEn1qQFB9YMlIWD/85ecB4NMydyvHWxw22PpnFJvCRNR/+vhSQLifEcJCnJJ5Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:47:22.864392Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.08129","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0045e13b1ff8d632869e42508f4662f22ac7417439d9cad90634143d1769e76","sha256:f51c6b457fa281a7eb1e972d3d62edf4f29d85c6d6f1251eb1598918e532f3af"],"state_sha256":"a71e8b0d1945d5a1ebd0d132a0ea7c8f409667f4c0c41309ef3bfc06b24449d5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SA/Ws+1bJYcp+JaqZ2By/FSg86dn8hTzth4+f6BSZcfaNZlUfwt1973gp24oOsmHG1RjV65VL/RGI69WJEWLCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T06:26:01.221275Z","bundle_sha256":"c67e26fbca41b19ea000182257863700d8a6e51a96bfbedec0827685aa175055"}}