{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:6NOZBDGEWVD5FL26ZGY37R5PKV","short_pith_number":"pith:6NOZBDGE","canonical_record":{"source":{"id":"1903.04527","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-11T18:28:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3e396095e2cb23f348f4573474aef8166894e4016f23be23b8ea07a58f701d3a","abstract_canon_sha256":"eb775735c6d862c026a791a367efb60d9592d40a5890bda326cd96517b3b5752"},"schema_version":"1.0"},"canonical_sha256":"f35d908cc4b547d2af5ec9b1bfc7af5573ca532d3b4f399a61e0baab1c3e64d3","source":{"kind":"arxiv","id":"1903.04527","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.04527","created_at":"2026-05-17T23:51:29Z"},{"alias_kind":"arxiv_version","alias_value":"1903.04527v1","created_at":"2026-05-17T23:51:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.04527","created_at":"2026-05-17T23:51:29Z"},{"alias_kind":"pith_short_12","alias_value":"6NOZBDGEWVD5","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"6NOZBDGEWVD5FL26","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"6NOZBDGE","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:6NOZBDGEWVD5FL26ZGY37R5PKV","target":"record","payload":{"canonical_record":{"source":{"id":"1903.04527","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-11T18:28:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3e396095e2cb23f348f4573474aef8166894e4016f23be23b8ea07a58f701d3a","abstract_canon_sha256":"eb775735c6d862c026a791a367efb60d9592d40a5890bda326cd96517b3b5752"},"schema_version":"1.0"},"canonical_sha256":"f35d908cc4b547d2af5ec9b1bfc7af5573ca532d3b4f399a61e0baab1c3e64d3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:29.378788Z","signature_b64":"BhMnvLm40Et/Id5lz8gIw3/Xq39wm5CkNWKlFXPX9eMyCXwZbvt+9nhDSzIy2uzd/yrdYHnO3+lzFNS9pjMRAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f35d908cc4b547d2af5ec9b1bfc7af5573ca532d3b4f399a61e0baab1c3e64d3","last_reissued_at":"2026-05-17T23:51:29.378394Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:29.378394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.04527","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":"2026-05-17T23:51:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+RQ8BR7sLqVSCfxXm3PfXDdudfdn4EogSuAwZVIKBJg6dRy0PmgazQOzl5n++zLBaeUvVWVpZaV3sq1LzX7SCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T12:52:27.655194Z"},"content_sha256":"cf3d10be87901b5172c223e5b90ae99be47f482d421bca44eb3ef98d6c911404","schema_version":"1.0","event_id":"sha256:cf3d10be87901b5172c223e5b90ae99be47f482d421bca44eb3ef98d6c911404"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:6NOZBDGEWVD5FL26ZGY37R5PKV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jie Wang, Lara Codec\\`a, TianShu Chu, Zhaojian Li","submitted_at":"2019-03-11T18:28:58Z","abstract_excerpt":"Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of the joint action space. Multi-agent RL (MARL) overcomes the scalability issue by distributing the global control to each local RL agent, but it introduces new challenges: now the environment becomes partially observable from the viewpoint of each local agent due to limited communication among agent"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.04527","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":"2026-05-17T23:51:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fX1s7lNIC4+UWBgs+WPDu1ZtF0NGZsDlfU1vAk1Dvhx+3hPrgeIyb3wwO3l6eoAAhMf274We4ZdO2QEJs9B5Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T12:52:27.655564Z"},"content_sha256":"0642b929be39205251d0cfd998a2d3cec39aadf24abc7545282f1117e4f184f8","schema_version":"1.0","event_id":"sha256:0642b929be39205251d0cfd998a2d3cec39aadf24abc7545282f1117e4f184f8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6NOZBDGEWVD5FL26ZGY37R5PKV/bundle.json","state_url":"https://pith.science/pith/6NOZBDGEWVD5FL26ZGY37R5PKV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6NOZBDGEWVD5FL26ZGY37R5PKV/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":"2026-06-28T12:52:27Z","links":{"resolver":"https://pith.science/pith/6NOZBDGEWVD5FL26ZGY37R5PKV","bundle":"https://pith.science/pith/6NOZBDGEWVD5FL26ZGY37R5PKV/bundle.json","state":"https://pith.science/pith/6NOZBDGEWVD5FL26ZGY37R5PKV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6NOZBDGEWVD5FL26ZGY37R5PKV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:6NOZBDGEWVD5FL26ZGY37R5PKV","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":"eb775735c6d862c026a791a367efb60d9592d40a5890bda326cd96517b3b5752","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-11T18:28:58Z","title_canon_sha256":"3e396095e2cb23f348f4573474aef8166894e4016f23be23b8ea07a58f701d3a"},"schema_version":"1.0","source":{"id":"1903.04527","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.04527","created_at":"2026-05-17T23:51:29Z"},{"alias_kind":"arxiv_version","alias_value":"1903.04527v1","created_at":"2026-05-17T23:51:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.04527","created_at":"2026-05-17T23:51:29Z"},{"alias_kind":"pith_short_12","alias_value":"6NOZBDGEWVD5","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"6NOZBDGEWVD5FL26","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"6NOZBDGE","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:0642b929be39205251d0cfd998a2d3cec39aadf24abc7545282f1117e4f184f8","target":"graph","created_at":"2026-05-17T23:51:29Z","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":"Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of the joint action space. Multi-agent RL (MARL) overcomes the scalability issue by distributing the global control to each local RL agent, but it introduces new challenges: now the environment becomes partially observable from the viewpoint of each local agent due to limited communication among agent","authors_text":"Jie Wang, Lara Codec\\`a, TianShu Chu, Zhaojian Li","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-11T18:28:58Z","title":"Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.04527","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:cf3d10be87901b5172c223e5b90ae99be47f482d421bca44eb3ef98d6c911404","target":"record","created_at":"2026-05-17T23:51:29Z","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":"eb775735c6d862c026a791a367efb60d9592d40a5890bda326cd96517b3b5752","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-11T18:28:58Z","title_canon_sha256":"3e396095e2cb23f348f4573474aef8166894e4016f23be23b8ea07a58f701d3a"},"schema_version":"1.0","source":{"id":"1903.04527","kind":"arxiv","version":1}},"canonical_sha256":"f35d908cc4b547d2af5ec9b1bfc7af5573ca532d3b4f399a61e0baab1c3e64d3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f35d908cc4b547d2af5ec9b1bfc7af5573ca532d3b4f399a61e0baab1c3e64d3","first_computed_at":"2026-05-17T23:51:29.378394Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:29.378394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BhMnvLm40Et/Id5lz8gIw3/Xq39wm5CkNWKlFXPX9eMyCXwZbvt+9nhDSzIy2uzd/yrdYHnO3+lzFNS9pjMRAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:29.378788Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.04527","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cf3d10be87901b5172c223e5b90ae99be47f482d421bca44eb3ef98d6c911404","sha256:0642b929be39205251d0cfd998a2d3cec39aadf24abc7545282f1117e4f184f8"],"state_sha256":"774023a0b325d119331a22c392fdb57da886385866e8e3f6e2343ddd3f859d77"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CSsIov2EgmQhemUw8R/j+g31kpYAgctXcR6uWMJdzHxUS96pZJvnC5WhPoJ0ONS2YhRQoxe3YtSX9iFL3HjoBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T12:52:27.657568Z","bundle_sha256":"1a0f12f6a91923adc9d0f3fd53229afb8622b4a3173a856be2a191d428a08898"}}