{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:S2JVENJWMM5CG2SJGKLB5LAMNZ","short_pith_number":"pith:S2JVENJW","schema_version":"1.0","canonical_sha256":"9693523536633a236a4932961eac0c6e766dab5c46badefd4ce1b358f497b867","source":{"kind":"arxiv","id":"1906.00431","version":1},"attestation_state":"computed","paper":{"title":"An Empirical Study on Hyperparameters and their Interdependence for RL Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jacob Jackson, Xingyou Song, Yilun Du","submitted_at":"2019-06-02T16:01:17Z","abstract_excerpt":"Recent results in Reinforcement Learning (RL) have shown that agents with limited training environments are susceptible to a large amount of overfitting across many domains. A key challenge for RL generalization is to quantitatively explain the effects of changing parameters on testing performance. Such parameters include architecture, regularization, and RL-dependent variables such as discount factor and action stochasticity. We provide empirical results that show complex and interdependent relationships between hyperparameters and generalization. We further show that several empirical metric"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1906.00431","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-02T16:01:17Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"75a57038a0a7205561e1612be870b2a7bbebdcf5f8b186263d99372470cd1d97","abstract_canon_sha256":"ee68550285d3362ac2e07c8a2cfa3cb2e60267f436916e61546ccbb2ec0a8c69"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:27.102109Z","signature_b64":"A8zkGqmKtGBaJopDNvrCUAey/ZLy0OskmdVBKKqHSxjcxZ9/41kH3fRf2dVfxIqqv2tUyHxIa/yd4+G73d9AAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9693523536633a236a4932961eac0c6e766dab5c46badefd4ce1b358f497b867","last_reissued_at":"2026-05-17T23:44:27.101454Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:27.101454Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Empirical Study on Hyperparameters and their Interdependence for RL Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jacob Jackson, Xingyou Song, Yilun Du","submitted_at":"2019-06-02T16:01:17Z","abstract_excerpt":"Recent results in Reinforcement Learning (RL) have shown that agents with limited training environments are susceptible to a large amount of overfitting across many domains. A key challenge for RL generalization is to quantitatively explain the effects of changing parameters on testing performance. Such parameters include architecture, regularization, and RL-dependent variables such as discount factor and action stochasticity. We provide empirical results that show complex and interdependent relationships between hyperparameters and generalization. We further show that several empirical metric"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00431","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1906.00431","created_at":"2026-05-17T23:44:27.101545+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.00431v1","created_at":"2026-05-17T23:44:27.101545+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00431","created_at":"2026-05-17T23:44:27.101545+00:00"},{"alias_kind":"pith_short_12","alias_value":"S2JVENJWMM5C","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"S2JVENJWMM5CG2SJ","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"S2JVENJW","created_at":"2026-05-18T12:33:27.125529+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S2JVENJWMM5CG2SJGKLB5LAMNZ","json":"https://pith.science/pith/S2JVENJWMM5CG2SJGKLB5LAMNZ.json","graph_json":"https://pith.science/api/pith-number/S2JVENJWMM5CG2SJGKLB5LAMNZ/graph.json","events_json":"https://pith.science/api/pith-number/S2JVENJWMM5CG2SJGKLB5LAMNZ/events.json","paper":"https://pith.science/paper/S2JVENJW"},"agent_actions":{"view_html":"https://pith.science/pith/S2JVENJWMM5CG2SJGKLB5LAMNZ","download_json":"https://pith.science/pith/S2JVENJWMM5CG2SJGKLB5LAMNZ.json","view_paper":"https://pith.science/paper/S2JVENJW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.00431&json=true","fetch_graph":"https://pith.science/api/pith-number/S2JVENJWMM5CG2SJGKLB5LAMNZ/graph.json","fetch_events":"https://pith.science/api/pith-number/S2JVENJWMM5CG2SJGKLB5LAMNZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S2JVENJWMM5CG2SJGKLB5LAMNZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S2JVENJWMM5CG2SJGKLB5LAMNZ/action/storage_attestation","attest_author":"https://pith.science/pith/S2JVENJWMM5CG2SJGKLB5LAMNZ/action/author_attestation","sign_citation":"https://pith.science/pith/S2JVENJWMM5CG2SJGKLB5LAMNZ/action/citation_signature","submit_replication":"https://pith.science/pith/S2JVENJWMM5CG2SJGKLB5LAMNZ/action/replication_record"}},"created_at":"2026-05-17T23:44:27.101545+00:00","updated_at":"2026-05-17T23:44:27.101545+00:00"}