{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:INVXC2YHEGJMGFRY4URUSFCDLW","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":"1cb5fce260fa176a1a2fd185d4e380ab417ffecc1785e804942306ca57140639","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-01T21:54:18Z","title_canon_sha256":"ec2ccf810a0e0eaddee84cddb8516eff69457ef313127ebd5bdeddb82c373208"},"schema_version":"1.0","source":{"id":"1806.01265","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.01265","created_at":"2026-05-18T00:11:17Z"},{"alias_kind":"arxiv_version","alias_value":"1806.01265v2","created_at":"2026-05-18T00:11:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.01265","created_at":"2026-05-18T00:11:17Z"},{"alias_kind":"pith_short_12","alias_value":"INVXC2YHEGJM","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"INVXC2YHEGJMGFRY","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"INVXC2YH","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:1f2865362cddd9f4b7084bf74e37f21d88b15b33d054ec46dade03e5b7cdaf09","target":"graph","created_at":"2026-05-18T00:11:17Z","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":"Learning a generative model is a key component of model-based reinforcement learning. Though learning a good model in the tabular setting is a simple task, learning a useful model in the approximate setting is challenging. In this context, an important question is the loss function used for model learning as varying the loss function can have a remarkable impact on effectiveness of planning. Recently Farahmand et al. (2017) proposed a value-aware model learning (VAML) objective that captures the structure of value function during model learning. Using tools from Asadi et al. (2018), we show th","authors_text":"Dipendra Misra, Evan Cater, Kavosh Asadi, Michael L. Littman","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-01T21:54:18Z","title":"Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.01265","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":"sha256:72ed8e4bee9ceeeee15eae51b8a6f55b20a2cd38689be98e83a06d7f917b2808","target":"record","created_at":"2026-05-18T00:11:17Z","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":"1cb5fce260fa176a1a2fd185d4e380ab417ffecc1785e804942306ca57140639","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-01T21:54:18Z","title_canon_sha256":"ec2ccf810a0e0eaddee84cddb8516eff69457ef313127ebd5bdeddb82c373208"},"schema_version":"1.0","source":{"id":"1806.01265","kind":"arxiv","version":2}},"canonical_sha256":"436b716b072192c31638e5234914435dbe93ab7f4ecf9046126573c8e2bb5f39","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"436b716b072192c31638e5234914435dbe93ab7f4ecf9046126573c8e2bb5f39","first_computed_at":"2026-05-18T00:11:17.255892Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:17.255892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ow0jThKltcbwAKrIozh+sYhK1i7M+afxhHgRWw81tOXeEXgd/aLd9LICxGNEoQd3HYRxTKtSB2ynhNLBQ04vAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:17.256668Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.01265","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:72ed8e4bee9ceeeee15eae51b8a6f55b20a2cd38689be98e83a06d7f917b2808","sha256:1f2865362cddd9f4b7084bf74e37f21d88b15b33d054ec46dade03e5b7cdaf09"],"state_sha256":"b51f9a1b5b47721adf64087ca9e9b1919dac9f064b42549c4a763888184d665e"}