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      "statement": "# MOPO: uncertainty-penalized learned MDPs for offline policy optimization\n\nModified rewards in a learned dynamics model by an uncertainty penalty and optimized policies in the penalized MDP, connecting conservative model use to conditional return bounds.\n\n## Scientific scope\n\nThe lower-bound analysis requires an admissible model-error estimator and stated value-class assumptions, not merely a learned variance heuristic. Exact surrogate optimization differs from finite deep RL updates. This soft reward-penalty mechanism is distinct from MOReL's unknown-region absorbing state.\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\nTianhe Yu; Garrett Thomas; Lantao Yu; Stefano Ermon; James Zou; Sergey Levine; Chelsea Finn; Tengyu Ma\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- [MOPO: Model-based Offline Policy Optimization](https://arxiv.org/abs/2005.13239v1)\n",
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