{
 "@context": "https://schema.org",
 "@type": "Claim",
 "@id": "https://wulfkaal.github.io/positions/2026-08-08-154",
 "identifier": "kaal:position:2026-08-08-154",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Streaming Reinforcement Learning Your Way Agent Characterization Through Policy Regulariza Ad45Bf1Af0",
 "text": "Reinforcement Learning Your Way states that the complexity of state-of-the-art reinforcement-learning algorithms produces opacity that inhibits explainability and understanding. This independently corresponds to Kaal's broader critique of current explainable RL. The abstract does not establish Kaal's more specific claims about toy examples, user testing, visualization complexity, or open-source practice.",
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
 "datePublished": "2026-08-08",
 "dateModified": "2026-08-08",
 "creativeWorkStatus": "Affirmed",
 "responseType": "agreement",
 "keywords": [
  "ai-and-agents",
  "historical-response",
  "scholarly-literature",
  "crossref"
 ],
 "scope_conditions": [
  "The response is limited to the retrieved source proposition and mapped Kaal claim unless fuller source review supports a broader conclusion.",
  "External evidence level: abstract indexed.",
  "Mapping review tier: substantively reviewed abstract-level agreement.",
  "Primary mapping confidence: 0.62.",
  "The primary mapping cleared the automated ambiguity test; substantive scope remains review-bound.",
  "Evidence is limited to an exact proposition in a Crossref-indexed abstract; full text was not reviewed.",
  "No relationship is treated as external endorsement, citation, causation, or validation of a broader Kaal claim.",
  "The source states that the complexity of state-of-the-art reinforcement-learning algorithms produces opacity that inhibits explainability and understanding. This independently corresponds to Kaal's broader critique that explainable-RL research has not yet produced usable explanations; it does not establish Kaal's more specific claims about toy examples, user testing, visualization complexity, or open-source practice."
 ],
 "currentDebate": {
  "name": "Reinforcement Learning Your Way: Agent Characterization through Policy Regularization",
  "url": "https://doi.org/10.3390/ai3020015"
 },
 "extends": {
  "identifier": "kaal:claim:4855607-013",
  "url": "https://wulfkaal.github.io/claims/4855607-013",
  "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
  "paper": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance",
  "authors": [
   "Wulf A. Kaal"
  ],
  "year": "2024",
  "ssrn": "https://ssrn.com/abstract=4855607",
  "source_pdf_sha256": "eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113"
 },
 "isBasedOn": [
  {
   "@id": "https://wulfkaal.github.io/claims/4855607-013"
  },
  {
   "@type": "CreativeWork",
   "name": "Reinforcement Learning Your Way: Agent Characterization through Policy Regularization",
   "url": "https://doi.org/10.3390/ai3020015"
  }
 ],
 "batch_id": "kaal-review:2026-08-08:continuous-crossref-0015-remainder-oldest-0050-reviewed-v1",
 "review_provenance": "https://kaal-signal-desk.wulf577462.chatgpt.site/#review",
 "publicationStatus": "public",
 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
 "isPartOf": {
  "@id": "https://wulfkaal.github.io/positions/index.json"
 },
 "version": "1.0",
 "canonical_url": "https://wulfkaal.github.io/positions/2026-08-08-154",
 "canonicalForm": "https://wulfkaal.github.io/positions/2026-08-08-154.md",
 "candidateId": "kaal:response-draft:2026-08-08:e14fab787c178295fb68",
 "evidenceLevel": "abstract indexed",
 "reviewTier": "substantively reviewed abstract-level agreement",
 "mappingConfidence": 0.62,
 "mappingAmbiguous": false,
 "mappingMethod": "complete-corpus source-proposition mechanism and scope adjudication",
 "mappingWhyRelevant": "The source states that the complexity of state-of-the-art reinforcement-learning algorithms produces opacity that inhibits explainability and understanding. This independently corresponds to Kaal's broader critique that explainable-RL research has not yet produced usable explanations; it does not establish Kaal's more specific claims about toy examples, user testing, visualization complexity, or open-source practice.",
 "sourceProvenance": {
  "source": "crossref",
  "sourceRecordId": "10.3390/ai3020015",
  "queryId": "concept:8542e1d4a2a4",
  "queryText": "AI agent reputation",
  "canonicalUrl": "https://doi.org/10.3390/ai3020015",
  "doi": "10.3390/ai3020015",
  "externalIds": {
   "DOI": "10.3390/ai3020015",
   "Crossref": "10.3390/ai3020015"
  },
  "retrievedAt": "2026-08-09T12:14:24.778Z",
  "providerPage": 1,
  "rawObservationSha256": "86899ad46ab25935e9b7e48e4d8e8f1044fde5565749a91a0dc3a3e3262342bb",
  "inputSnapshotSha256": "2bab6ae028a03ca5d076ff5417f551f5a465ee2a375e93a2a8bec6487701f61b",
  "inputLine": 7190,
  "chunkId": "crossref-00001",
  "workId": "work:doi:10.3390/ai3020015",
  "workAuthors": [
   "Charl Maree",
   "Christian Omlin"
  ],
  "workPublishedAt": "2022-03-24",
  "identityKeys": [
   "doi:10.3390/ai3020015",
   "crossref:10.3390/ai3020015",
   "url:https://doi.org/10.3390/ai3020015",
   "title:afa87ff1da23dfa044ed6d74",
   "proposition:f6ff0a37d3a6e8590ae5d04ff85736b4dad4db04604db6d80602054050962211"
  ],
  "sourceProposition": "The increased complexity of state-of-the-art reinforcement learning (RL) algorithms has resulted in an opacity that inhibits explainability and understanding.",
  "sourcePropositionSha256": "f6a3f8627240b3d77a7b35b451dc1693bb2b26e7774161d90dcc0eb9016daf0d",
  "sourcePropositionIndex": 0,
  "claimMappings": [
   {
    "claimId": "kaal:claim:4855607-013",
    "claimUrl": "https://wulfkaal.github.io/claims/4855607-013",
    "rank": 1,
    "confidence": 0.62,
    "method": "complete-corpus source-proposition mechanism and scope adjudication",
    "whyRelevant": "The source states that the complexity of state-of-the-art reinforcement-learning algorithms produces opacity that inhibits explainability and understanding. This independently corresponds to Kaal's broader critique that explainable-RL research has not yet produced usable explanations; it does not establish Kaal's more specific claims about toy examples, user testing, visualization complexity, or open-source practice.",
    "ambiguous": false
   }
  ],
  "substantiveReview": {
   "disposition": "retained",
   "reviewVersion": "kaal-continuous-crossref-substantive-review-v1",
   "reviewedAt": "2026-08-09T20:20:14.900Z",
   "reviewer": "Codex continuous substantive reviewer",
   "rationale": "The source states that the complexity of state-of-the-art reinforcement-learning algorithms produces opacity that inhibits explainability and understanding. This independently corresponds to Kaal's broader critique that explainable-RL research has not yet produced usable explanations; it does not establish Kaal's more specific claims about toy examples, user testing, visualization complexity, or open-source practice.",
   "limitations": [
    "Evidence is limited to an exact proposition in a Crossref-indexed abstract; full text was not reviewed.",
    "No relationship is treated as external endorsement, citation, causation, or validation of a broader Kaal claim.",
    "The source states that the complexity of state-of-the-art reinforcement-learning algorithms produces opacity that inhibits explainability and understanding. This independently corresponds to Kaal's broader critique that explainable-RL research has not yet produced usable explanations; it does not establish Kaal's more specific claims about toy examples, user testing, visualization complexity, or open-source practice."
   ],
   "sourceIdentity": {
    "status": "verified",
    "source": "Crossref REST work record",
    "doi": "10.3390/ai3020015",
    "title": "Reinforcement Learning Your Way: Agent Characterization through Policy Regularization",
    "authors": [
     "Charl Maree",
     "Christian Omlin"
    ],
    "language": "en",
    "publisher": "MDPI AG",
    "checkedAt": "2026-08-09T20:20:14.900Z",
    "responseBodySha256": "61f8f6dd7fa799f21991077e8262e5620fa83b98ebcf6500b9bacdd0d22f33d9",
    "providerReceipt": {
     "path": "outputs/historical-backfill-2026-08-08-crossref/raw-receipts/crossref-015-00001.json",
     "sha256": "77d874f2fd7fd9cb6ec31b6bf0fdd667ff7b7cbff6877eba58760481ebef6fac",
     "byteLength": 1525669
    }
   },
   "independentRationale": "The exact proposition is present in the live Crossref abstract and the deposited reference list contains no Kaal-authored reference; the relationship is limited to the independently stated proposition.",
   "nonOverlap": {
    "candidateIdMatches": 0,
    "canonicalUrlMatches": 0,
    "propositionHashMatches": 0
   }
  }
 },
 "userAffirmation": "Automatically authorized under standing authority receipt kaal-standing-publication-authorization:2026-08-01:hourly-reviewed-batches, SHA-256 e2126054b58bb4e88db65c334ef4fc8ae78dcaadc63543c408133c4eefceb9b1, limited to this substantively reviewed batch and the unchanged 5,033 scholarly claims.",
 "sha256": "aace9b6bad1e03298ad4a3887892ba218244d7d9d86396b96588c76859dafcc4"
}
