{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:5ZKOA2SORD3ESHZ7IFA4WUTJUA","short_pith_number":"pith:5ZKOA2SO","canonical_record":{"source":{"id":"2409.19391","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-28T15:57:24Z","cross_cats_sorted":[],"title_canon_sha256":"3136c55473f2f8b7b3fcdfb08d3f590ebb07c5d982c2b247eb0daa50c280fd90","abstract_canon_sha256":"fa9c46a6168cd5ba8b6139a0893a758f286ed2bd8b932463245da31ad28107f9"},"schema_version":"1.0"},"canonical_sha256":"ee54e06a4e88f6491f3f4141cb5269a017f64f18aa957292047b1a2c2224e127","source":{"kind":"arxiv","id":"2409.19391","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.19391","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"arxiv_version","alias_value":"2409.19391v1","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.19391","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"pith_short_12","alias_value":"5ZKOA2SORD3E","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"pith_short_16","alias_value":"5ZKOA2SORD3ESHZ7","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"pith_short_8","alias_value":"5ZKOA2SO","created_at":"2026-07-05T09:13:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:5ZKOA2SORD3ESHZ7IFA4WUTJUA","target":"record","payload":{"canonical_record":{"source":{"id":"2409.19391","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-28T15:57:24Z","cross_cats_sorted":[],"title_canon_sha256":"3136c55473f2f8b7b3fcdfb08d3f590ebb07c5d982c2b247eb0daa50c280fd90","abstract_canon_sha256":"fa9c46a6168cd5ba8b6139a0893a758f286ed2bd8b932463245da31ad28107f9"},"schema_version":"1.0"},"canonical_sha256":"ee54e06a4e88f6491f3f4141cb5269a017f64f18aa957292047b1a2c2224e127","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:14.692906Z","signature_b64":"5hHkoN875iOpGvOIYYqd5faSGZUKxsSeYi7t3DLCMe8eu4Jb4Kw6mcjYhfC729KU0zVwcLkKgmmoAXCXbpcoCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee54e06a4e88f6491f3f4141cb5269a017f64f18aa957292047b1a2c2224e127","last_reissued_at":"2026-07-05T09:13:14.692442Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:14.692442Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.19391","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":"2026-07-05T09:13:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZJo/U6mzvjwdiIfZTl/CLmc2DP5Cwim2VCpS/jMqgcCM9WEopQxqKOaKnqtXU8/Trgx+o3FZVd0gQ/YzyHgaCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:41:32.899472Z"},"content_sha256":"a385b48dfb99b720b5f33f645541e1253602a268561df7d4b8d256e17330b9de","schema_version":"1.0","event_id":"sha256:a385b48dfb99b720b5f33f645541e1253602a268561df7d4b8d256e17330b9de"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:5ZKOA2SORD3ESHZ7IFA4WUTJUA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ling Pan, Longbo Huang, Pihe Hu, Shaolong Li, Zhuoran Li","submitted_at":"2024-09-28T15:57:24Z","abstract_excerpt":"Deep Multi-agent Reinforcement Learning (MARL) relies on neural networks with numerous parameters in multi-agent scenarios, often incurring substantial computational overhead. Consequently, there is an urgent need to expedite training and enable model compression in MARL. This paper proposes the utilization of dynamic sparse training (DST), a technique proven effective in deep supervised learning tasks, to alleviate the computational burdens in MARL training. However, a direct adoption of DST fails to yield satisfactory MARL agents, leading to breakdowns in value learning within deep sparse va"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.19391","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/2409.19391/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":"2026-07-05T09:13:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QrcvOnAIXjTqb8675cy5lPkjJxuisuTshd2k3u35+/ao8Hktt3pbRpTUVmDqXkFfycZsCQU1qPVcRLJoO30uAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:41:32.900399Z"},"content_sha256":"5317e286ff3677fe6b7bc03c998e85bbedac8c9ef79d90bf1055f62430833d0c","schema_version":"1.0","event_id":"sha256:5317e286ff3677fe6b7bc03c998e85bbedac8c9ef79d90bf1055f62430833d0c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5ZKOA2SORD3ESHZ7IFA4WUTJUA/bundle.json","state_url":"https://pith.science/pith/5ZKOA2SORD3ESHZ7IFA4WUTJUA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5ZKOA2SORD3ESHZ7IFA4WUTJUA/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":"2026-08-03T18:41:32Z","links":{"resolver":"https://pith.science/pith/5ZKOA2SORD3ESHZ7IFA4WUTJUA","bundle":"https://pith.science/pith/5ZKOA2SORD3ESHZ7IFA4WUTJUA/bundle.json","state":"https://pith.science/pith/5ZKOA2SORD3ESHZ7IFA4WUTJUA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5ZKOA2SORD3ESHZ7IFA4WUTJUA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:5ZKOA2SORD3ESHZ7IFA4WUTJUA","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":"fa9c46a6168cd5ba8b6139a0893a758f286ed2bd8b932463245da31ad28107f9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-28T15:57:24Z","title_canon_sha256":"3136c55473f2f8b7b3fcdfb08d3f590ebb07c5d982c2b247eb0daa50c280fd90"},"schema_version":"1.0","source":{"id":"2409.19391","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.19391","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"arxiv_version","alias_value":"2409.19391v1","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.19391","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"pith_short_12","alias_value":"5ZKOA2SORD3E","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"pith_short_16","alias_value":"5ZKOA2SORD3ESHZ7","created_at":"2026-07-05T09:13:14Z"},{"alias_kind":"pith_short_8","alias_value":"5ZKOA2SO","created_at":"2026-07-05T09:13:14Z"}],"graph_snapshots":[{"event_id":"sha256:5317e286ff3677fe6b7bc03c998e85bbedac8c9ef79d90bf1055f62430833d0c","target":"graph","created_at":"2026-07-05T09:13:14Z","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/2409.19391/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep Multi-agent Reinforcement Learning (MARL) relies on neural networks with numerous parameters in multi-agent scenarios, often incurring substantial computational overhead. Consequently, there is an urgent need to expedite training and enable model compression in MARL. This paper proposes the utilization of dynamic sparse training (DST), a technique proven effective in deep supervised learning tasks, to alleviate the computational burdens in MARL training. However, a direct adoption of DST fails to yield satisfactory MARL agents, leading to breakdowns in value learning within deep sparse va","authors_text":"Ling Pan, Longbo Huang, Pihe Hu, Shaolong Li, Zhuoran Li","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-28T15:57:24Z","title":"Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.19391","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:a385b48dfb99b720b5f33f645541e1253602a268561df7d4b8d256e17330b9de","target":"record","created_at":"2026-07-05T09:13:14Z","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":"fa9c46a6168cd5ba8b6139a0893a758f286ed2bd8b932463245da31ad28107f9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-28T15:57:24Z","title_canon_sha256":"3136c55473f2f8b7b3fcdfb08d3f590ebb07c5d982c2b247eb0daa50c280fd90"},"schema_version":"1.0","source":{"id":"2409.19391","kind":"arxiv","version":1}},"canonical_sha256":"ee54e06a4e88f6491f3f4141cb5269a017f64f18aa957292047b1a2c2224e127","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ee54e06a4e88f6491f3f4141cb5269a017f64f18aa957292047b1a2c2224e127","first_computed_at":"2026-07-05T09:13:14.692442Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:13:14.692442Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5hHkoN875iOpGvOIYYqd5faSGZUKxsSeYi7t3DLCMe8eu4Jb4Kw6mcjYhfC729KU0zVwcLkKgmmoAXCXbpcoCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:13:14.692906Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.19391","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a385b48dfb99b720b5f33f645541e1253602a268561df7d4b8d256e17330b9de","sha256:5317e286ff3677fe6b7bc03c998e85bbedac8c9ef79d90bf1055f62430833d0c"],"state_sha256":"9082d2919797846e0dbca98dbbd9696b57542927477f817f8c18a62105df9f53"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tF+BuXnBWFMdpDC5F2yJw8fBJJWRHs6+LgCnuIVHu5cxgSkBXPHRLug/1lhJNWKn/73QtkkXHP8oI2Nivdg+Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T18:41:32.910578Z","bundle_sha256":"e7f7bad805e2c5cd4a2d12c5891b503651219a5724ab9ebb9450c226880ba2bf"}}