{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ZZGYJFHAAS43ROVISSZ5XUTGND","short_pith_number":"pith:ZZGYJFHA","schema_version":"1.0","canonical_sha256":"ce4d8494e004b9b8baa894b3dbd26668ded9d7b5d63f9e63d819b237561828b4","source":{"kind":"arxiv","id":"2205.14557","version":2},"attestation_state":"computed","paper":{"title":"Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Huangyuan Su, Jieyu Zhang, Qiang He, Xinwen Hou","submitted_at":"2022-05-29T02:29:32Z","abstract_excerpt":"Deep reinforcement learning (DRL) gives the promise that an agent learns good policy from high-dimensional information, whereas representation learning removes irrelevant and redundant information and retains pertinent information. In this work, we demonstrate that the learned representation of the $Q$-network and its target $Q$-network should, in theory, satisfy a favorable distinguishable representation property. Specifically, there exists an upper bound on the representation similarity of the value functions of two adjacent time steps in a typical DRL setting. However, through illustrative "},"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":"2205.14557","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-29T02:29:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"298b6a7ffe1ac1522954c01ff0de9e89e036bd3fb5189fcf5e7fe5a6f6827ab6","abstract_canon_sha256":"b8f90a0f6922c89ee19ef8963de56954282b6a7e335da1a1077f7abd78d173fc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:03:24.154205Z","signature_b64":"Bg+u4O0k2bgqjGM/xs8VyztMYYJNpUrICIpb4fQR2blIYTrp5CXe9XfWaednwZTp5JNlUnHCYKAJfXxzFnlWAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce4d8494e004b9b8baa894b3dbd26668ded9d7b5d63f9e63d819b237561828b4","last_reissued_at":"2026-07-05T06:03:24.153806Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:03:24.153806Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Huangyuan Su, Jieyu Zhang, Qiang He, Xinwen Hou","submitted_at":"2022-05-29T02:29:32Z","abstract_excerpt":"Deep reinforcement learning (DRL) gives the promise that an agent learns good policy from high-dimensional information, whereas representation learning removes irrelevant and redundant information and retains pertinent information. In this work, we demonstrate that the learned representation of the $Q$-network and its target $Q$-network should, in theory, satisfy a favorable distinguishable representation property. Specifically, there exists an upper bound on the representation similarity of the value functions of two adjacent time steps in a typical DRL setting. However, through illustrative "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.14557","kind":"arxiv","version":2},"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/2205.14557/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2205.14557","created_at":"2026-07-05T06:03:24.153860+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.14557v2","created_at":"2026-07-05T06:03:24.153860+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.14557","created_at":"2026-07-05T06:03:24.153860+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZZGYJFHAAS43","created_at":"2026-07-05T06:03:24.153860+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZZGYJFHAAS43ROVI","created_at":"2026-07-05T06:03:24.153860+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZZGYJFHA","created_at":"2026-07-05T06:03:24.153860+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/ZZGYJFHAAS43ROVISSZ5XUTGND","json":"https://pith.science/pith/ZZGYJFHAAS43ROVISSZ5XUTGND.json","graph_json":"https://pith.science/api/pith-number/ZZGYJFHAAS43ROVISSZ5XUTGND/graph.json","events_json":"https://pith.science/api/pith-number/ZZGYJFHAAS43ROVISSZ5XUTGND/events.json","paper":"https://pith.science/paper/ZZGYJFHA"},"agent_actions":{"view_html":"https://pith.science/pith/ZZGYJFHAAS43ROVISSZ5XUTGND","download_json":"https://pith.science/pith/ZZGYJFHAAS43ROVISSZ5XUTGND.json","view_paper":"https://pith.science/paper/ZZGYJFHA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.14557&json=true","fetch_graph":"https://pith.science/api/pith-number/ZZGYJFHAAS43ROVISSZ5XUTGND/graph.json","fetch_events":"https://pith.science/api/pith-number/ZZGYJFHAAS43ROVISSZ5XUTGND/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZZGYJFHAAS43ROVISSZ5XUTGND/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZZGYJFHAAS43ROVISSZ5XUTGND/action/storage_attestation","attest_author":"https://pith.science/pith/ZZGYJFHAAS43ROVISSZ5XUTGND/action/author_attestation","sign_citation":"https://pith.science/pith/ZZGYJFHAAS43ROVISSZ5XUTGND/action/citation_signature","submit_replication":"https://pith.science/pith/ZZGYJFHAAS43ROVISSZ5XUTGND/action/replication_record"}},"created_at":"2026-07-05T06:03:24.153860+00:00","updated_at":"2026-07-05T06:03:24.153860+00:00"}