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        "description": "Worst-case bounds alone favor zero model rollout; justification for nonzero rollouts incorporates empirical model-generalization estimates. Bounded rewards, model error and policy shift control the theoretical statements. Practical finite updates are not an exact optimizer or unconditional monotonic-improvement theorem.",
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      "statement": "# MBPO: short model rollouts branched from real replay states\n\nTrained a probabilistic dynamics ensemble, branched short synthetic rollouts from real replay-buffer states and used model samples for soft actor-critic updates.\n\n## Scientific scope\n\nWorst-case bounds alone favor zero model rollout; justification for nonzero rollouts incorporates empirical model-generalization estimates. Bounded rewards, model error and policy shift control the theoretical statements. Practical finite updates are not an exact optimizer or unconditional monotonic-improvement theorem.\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\nMichael Janner; Justin Fu; Marvin Zhang; Sergey Levine\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- [When to Trust Your Model: Model-Based Policy Optimization](https://arxiv.org/abs/1906.08253v1)\n",
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