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      "statement": "# IMPALA decoupled actor-learners and V-trace correction\n\nDecouples trajectory-generating actors from batched learners and uses truncated-importance V-trace targets to compensate actor-policy lag in high-throughput multi-task RL.\n\n## Scientific scope\n\nFinite rho truncation evaluates a policy between behavior and target rather than exactly the current target; c truncation controls traces differently. Tabular/operator guarantees are not a convergence proof for arbitrary deep function approximation. First three authors are equal contributors; printed Volodymir Mnih is the source label, not an independently resolved identity correction.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nLasse Espeholt; Hubert Soyer; Remi Munos; Karen Simonyan; Volodymir Mnih; Tom Ward; Yotam Doron; Vlad Firoiu; Tim Harley; Iain Dunning; Shane Legg; Koray Kavukcuoglu\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://arxiv.org/pdf/1802.01561v1)\n",
      "summary": "Decouples trajectory-generating actors from batched learners and uses truncated-importance V-trace targets to compensate actor-policy lag in high-throughput multi-task RL.",
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