RESEARCH PAPERYear 2019

When to Trust Your Model: Model-Based Policy Optimization

Michael Janner; Justin Fu; Marvin Zhang; Sergey Levine

Classification

Major category: Foundational work
Quadrant: Not applicable
Architecture: Not applicable
Prediction paradigm: Not applicable
Subcategories: Classical world models & model-based RL Training optimization & distillation
Source review status: Explicit in survey

Category review. MBPO is pre-2026 general theory and methodology for controlling model bias through short model rollouts from real states. It teaches how learned dynamics augment a separate policy learner, a foundational MBRL contribution. Reading evidence

AT A GLANCE

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BibTeX

@article{trust,
  title={When to trust your model: Model-based policy optimization},
  author={Janner, Michael and Fu, Justin and Zhang, Marvin and Levine, Sergey},
  journal={NeurIPS},
  volume={32},
  year={2019}
}

Classification

Contribution

Abstract

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BibTeX

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