Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
When to Trust Your Model: Model-Based Policy Optimization
Authors: Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation aims to study two primary questions: (1) How well does MBPO perform on benchmark reinforcement learning tasks, compared to state-of-the-art model-based and model-free algorithms? (2) What conclusions can we draw about appropriate model usage? (Figure 2: Training curves of MBPO and five baselines on continuous control benchmarks. Solid curves depict the mean of five trials and shaded regions correspond to standard deviation among trials.) |
| Researcher Affiliation | Academia | Michael Janner Justin Fu Marvin Zhang Sergey Levine University of California, Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1 Monotonic Model-Based Policy Optimization; Algorithm 2 Model-Based Policy Optimization with Deep Reinforcement Learning |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology. |
| Open Datasets | Yes | We evaluate MBPO and these baselines on a set of MuJoCo continuous control tasks (Todorov et al., 2012) commonly used to evaluate model-free algorithms. |
| Dataset Splits | No | The paper mentions 'validation loss of the model' but does not provide specific details on the validation dataset split (e.g., percentages, sample counts, or explicit standard split references). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions software like MuJoCo, SAC, and PPO, but does not provide specific version numbers for any software components, which is required for reproducibility. |
| Experiment Setup | Yes | A full listing of the hyperparameters included in Algorithm 2 for all evaluation environments is given in Appendix C. |