logbook.json

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  "schema_version": 2,
  "title": "Reproduction: Online Robust Reinforcement Learning with General Function Approximation",
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  "tags": [
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    "paper-fcVsVQkjIc",
    "dr-rl",
    "function-approximation"
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  "updated_at": "2026-08-03T22:55:47+00:00",
  "root": {
    "slug": "index",
    "title": "Reproduction: Online Robust Reinforcement Learning with General Function Approximation",
    "file": "pages/index.md",
    "children": [
      {
        "slug": "executive-summary",
        "title": "Executive summary",
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      },
      {
        "slug": "claim-1",
        "title": "Claim 1: First regret guarantee for online DR-RL",
        "file": "pages/claim-1/page.md",
        "children": []
      },
      {
        "slug": "claim-2",
        "title": "Claim 2: Robust coverability C_rcov",
        "file": "pages/claim-2/page.md",
        "children": []
      },
      {
        "slug": "claim-3",
        "title": "Claim 3: Linear TV-RMDP specialization",
        "file": "pages/claim-3/page.md",
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      },
      {
        "slug": "claim-4",
        "title": "Claim 4: Table 1 sample-complexity comparison",
        "file": "pages/claim-4/page.md",
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      },
      {
        "slug": "claim-5",
        "title": "Claim 5: Dual-driven global confidence set",
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      },
      {
        "slug": "claim-6",
        "title": "Claim 6: Numerical regret-scaling experiments",
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      },
      {
        "slug": "conclusion",
        "title": "Conclusion",
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      }
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