logbook.json

{
  "schema_version": 1,
  "title": "Reproduction: Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates",
  "emoji": "🛡️",
  "space_id": "snaykey/repro-async-q-learning",
  "paper": {
    "openreview_id": "L5ZJv73k7x"
  },
  "tags": [
    "icml2026-repro",
    "paper-L5ZJv73k7x"
  ],
  "updated_at": "2026-07-30T16:28:22+00:00",
  "root": {
    "slug": "index",
    "title": "Reproduction: Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates",
    "file": "pages/index.md",
    "children": [
      {
        "slug": "claim-1-theorem-2-rate",
        "title": "Theorem 2 shows Robust Async-Q achieves a finite-time convergence rate of O(1/√T), matching classical asynchronous Q-learning, plus an additive O(√ε) term where ε∈[0,1/2) is the fraction of adversarially corrupted rewards (Section 3.1).",
        "file": "pages/claim-1-theorem-2-rate/page.md",
        "children": []
      },
      {
        "slug": "claim-2-theorem-3-lower-bound",
        "title": "Theorem 3 gives an information-theoretic lower bound of Ω(√ε), proving the additive corruption term in the upper bound is unavoidable (Section 4).",
        "file": "pages/claim-2-theorem-3-lower-bound/page.md",
        "children": []
      },
      {
        "slug": "claim-3-theorem-4-reward-agnostic",
        "title": "Theorem 4 extends the guarantee to a reward-agnostic setting where the algorithm needs no prior knowledge of the reward distribution's bounds (Section 5.1).",
        "file": "pages/claim-3-theorem-4-reward-agnostic/page.md",
        "children": []
      },
      {
        "slug": "claim-4-theorem-6-markovian",
        "title": "Theorem 6 extends the near-optimal finite-time rate to single-trajectory Markovian sampling with time-correlated data, with the bound inflated by the chain's mixing time (Section 6).",
        "file": "pages/claim-4-theorem-6-markovian/page.md",
        "children": []
      },
      {
        "slug": "claim-5-algorithm-1",
        "title": "Algorithm 1 (Robust Async-Q) combines a trimmed-mean reward estimator with an adaptive threshold G_t to filter corrupted rewards before the standard Q-learning update, requiring only knowledge of the corruption fraction ε and minimum state-action visitation probability λ_min (Algorithm 1, Assumption 1).",
        "file": "pages/claim-5-algorithm-1/page.md",
        "children": []
      },
      {
        "slug": "executive-summary",
        "title": "executive-summary",
        "file": "pages/executive-summary/page.md",
        "children": []
      },
      {
        "slug": "conclusion",
        "title": "conclusion",
        "file": "pages/conclusion/page.md",
        "children": []
      }
    ]
  }
}