{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:6V75XGON7JAQC3EZ7XATNXF42Q","short_pith_number":"pith:6V75XGON","canonical_record":{"source":{"id":"1906.10124","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2019-06-25T15:18:10Z","cross_cats_sorted":["cs.AI","cs.HC","cs.LG"],"title_canon_sha256":"3a774c3ad7f52ce990dfb6fe86bfc517eec5c54678a44b090f3d59a5b00c9f6d","abstract_canon_sha256":"b155ba08ae0be0107bccc0816b403c8ae0093ef792706ac0be982a245467f035"},"schema_version":"1.0"},"canonical_sha256":"f57fdb99cdfa41016c99fdc136dcbcd427ca2812a914ec8232ff048d7023ad38","source":{"kind":"arxiv","id":"1906.10124","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.10124","created_at":"2026-05-17T23:42:32Z"},{"alias_kind":"arxiv_version","alias_value":"1906.10124v1","created_at":"2026-05-17T23:42:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.10124","created_at":"2026-05-17T23:42:32Z"},{"alias_kind":"pith_short_12","alias_value":"6V75XGON7JAQ","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"6V75XGON7JAQC3EZ","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"6V75XGON","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:6V75XGON7JAQC3EZ7XATNXF42Q","target":"record","payload":{"canonical_record":{"source":{"id":"1906.10124","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2019-06-25T15:18:10Z","cross_cats_sorted":["cs.AI","cs.HC","cs.LG"],"title_canon_sha256":"3a774c3ad7f52ce990dfb6fe86bfc517eec5c54678a44b090f3d59a5b00c9f6d","abstract_canon_sha256":"b155ba08ae0be0107bccc0816b403c8ae0093ef792706ac0be982a245467f035"},"schema_version":"1.0"},"canonical_sha256":"f57fdb99cdfa41016c99fdc136dcbcd427ca2812a914ec8232ff048d7023ad38","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:32.126704Z","signature_b64":"WfV5xsVOoGV4kDtP9o1fYqYDTRaDDmvG8nnrQQ2U3MTZTNtk0aDYkO/lHbvwFnpj8q0wdFv4H7V36tvqehtTDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f57fdb99cdfa41016c99fdc136dcbcd427ca2812a914ec8232ff048d7023ad38","last_reissued_at":"2026-05-17T23:42:32.125977Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:32.125977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.10124","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":"2026-05-17T23:42:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dw7XB+I3sl8bXUtG3hRVzQtnH1ARKZszLdlDrE1FM8GRm6s3L2eJDmxa92jADRlP95rCmV7Qz/3PDBp3vP9cBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T20:06:25.786153Z"},"content_sha256":"243ed8956077669e6d6a605992325b31259380981afdda24d40cccc6ad6ca18d","schema_version":"1.0","event_id":"sha256:243ed8956077669e6d6a605992325b31259380981afdda24d40cccc6ad6ca18d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:6V75XGON7JAQC3EZ7XATNXF42Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On Multi-Agent Learning in Team Sports Games","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.LG"],"primary_cat":"cs.MA","authors_text":"Ahmad Beirami, Caedmon Somers, Igor Borovikov, Jason Rupert, Yunqi Zhao","submitted_at":"2019-06-25T15:18:10Z","abstract_excerpt":"In recent years, reinforcement learning has been successful in solving video games from Atari to Star Craft II. However, the end-to-end model-free reinforcement learning (RL) is not sample efficient and requires a significant amount of computational resources to achieve superhuman level performance. Model-free RL is also unlikely to produce human-like agents for playtesting and gameplaying AI in the development cycle of complex video games. In this paper, we present a hierarchical approach to training agents with the goal of achieving human-like style and high skill level in team sports games."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.10124","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":""},"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":"2026-05-17T23:42:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7NOQrVhjzB5pnyuwtEGQcGBUSCy0DjhVDOK23WaJIXfauyRJIKgvjNaZRJhE2/9Exbi4q3dVHbSDTJXcmBJOBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T20:06:25.786823Z"},"content_sha256":"bc6e298e970bf7a45bb24604379b57a6b89027b98597e4c065e61e1049046b6d","schema_version":"1.0","event_id":"sha256:bc6e298e970bf7a45bb24604379b57a6b89027b98597e4c065e61e1049046b6d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6V75XGON7JAQC3EZ7XATNXF42Q/bundle.json","state_url":"https://pith.science/pith/6V75XGON7JAQC3EZ7XATNXF42Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6V75XGON7JAQC3EZ7XATNXF42Q/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":"2026-05-26T20:06:25Z","links":{"resolver":"https://pith.science/pith/6V75XGON7JAQC3EZ7XATNXF42Q","bundle":"https://pith.science/pith/6V75XGON7JAQC3EZ7XATNXF42Q/bundle.json","state":"https://pith.science/pith/6V75XGON7JAQC3EZ7XATNXF42Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6V75XGON7JAQC3EZ7XATNXF42Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:6V75XGON7JAQC3EZ7XATNXF42Q","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":"b155ba08ae0be0107bccc0816b403c8ae0093ef792706ac0be982a245467f035","cross_cats_sorted":["cs.AI","cs.HC","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2019-06-25T15:18:10Z","title_canon_sha256":"3a774c3ad7f52ce990dfb6fe86bfc517eec5c54678a44b090f3d59a5b00c9f6d"},"schema_version":"1.0","source":{"id":"1906.10124","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.10124","created_at":"2026-05-17T23:42:32Z"},{"alias_kind":"arxiv_version","alias_value":"1906.10124v1","created_at":"2026-05-17T23:42:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.10124","created_at":"2026-05-17T23:42:32Z"},{"alias_kind":"pith_short_12","alias_value":"6V75XGON7JAQ","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"6V75XGON7JAQC3EZ","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"6V75XGON","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:bc6e298e970bf7a45bb24604379b57a6b89027b98597e4c065e61e1049046b6d","target":"graph","created_at":"2026-05-17T23:42:32Z","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"},"paper":{"abstract_excerpt":"In recent years, reinforcement learning has been successful in solving video games from Atari to Star Craft II. However, the end-to-end model-free reinforcement learning (RL) is not sample efficient and requires a significant amount of computational resources to achieve superhuman level performance. Model-free RL is also unlikely to produce human-like agents for playtesting and gameplaying AI in the development cycle of complex video games. In this paper, we present a hierarchical approach to training agents with the goal of achieving human-like style and high skill level in team sports games.","authors_text":"Ahmad Beirami, Caedmon Somers, Igor Borovikov, Jason Rupert, Yunqi Zhao","cross_cats":["cs.AI","cs.HC","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2019-06-25T15:18:10Z","title":"On Multi-Agent Learning in Team Sports Games"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.10124","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":"sha256:243ed8956077669e6d6a605992325b31259380981afdda24d40cccc6ad6ca18d","target":"record","created_at":"2026-05-17T23:42:32Z","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":"b155ba08ae0be0107bccc0816b403c8ae0093ef792706ac0be982a245467f035","cross_cats_sorted":["cs.AI","cs.HC","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2019-06-25T15:18:10Z","title_canon_sha256":"3a774c3ad7f52ce990dfb6fe86bfc517eec5c54678a44b090f3d59a5b00c9f6d"},"schema_version":"1.0","source":{"id":"1906.10124","kind":"arxiv","version":1}},"canonical_sha256":"f57fdb99cdfa41016c99fdc136dcbcd427ca2812a914ec8232ff048d7023ad38","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f57fdb99cdfa41016c99fdc136dcbcd427ca2812a914ec8232ff048d7023ad38","first_computed_at":"2026-05-17T23:42:32.125977Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:32.125977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WfV5xsVOoGV4kDtP9o1fYqYDTRaDDmvG8nnrQQ2U3MTZTNtk0aDYkO/lHbvwFnpj8q0wdFv4H7V36tvqehtTDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:32.126704Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.10124","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:243ed8956077669e6d6a605992325b31259380981afdda24d40cccc6ad6ca18d","sha256:bc6e298e970bf7a45bb24604379b57a6b89027b98597e4c065e61e1049046b6d"],"state_sha256":"e1e35176774006d96d0b7e9e2dce7ee74f077f28d4133974dc6db89e85cf5d43"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AjMD1xo8z77t0rj/bmn3TSzs3U9pNvBJKDlXYxF2yRnnRqzeIj5/DjgQ/3ZCrBtRbp5QM+Z8el+O7TuWEXJlDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T20:06:25.790368Z","bundle_sha256":"3aee5d296dba7f60973eafb59672f19ee5e3cf9539c106589794a83dedf825b0"}}