Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
MOPO: Model-based Offline Policy Optimization
Authors: Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y. Zou, Sergey Levine, Chelsea Finn, Tengyu Ma
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 21,257 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,242 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we aim to study the follow questions: (1) How does MOPO perform on standard offline RL benchmarks in comparison to prior state-of-the-art approaches? (2) Can MOPO solve tasks that require generalization to out-of-distribution behaviors? (3) How does each component in MOPO affect performance? |
| Researcher Affiliation | Academia | Stanford University1, UC Berkeley2 EMAIL |
| Pseudocode | Yes | Algorithm 1 Framework for Model-based Offline Policy Optimization (MOPO) with Reward Penalty |
| Open Source Code | Yes | The code is available online5. 5Code is released at https://github.com/tianheyu927/mopo. |
| Open Datasets | Yes | To answer question (1), we evaluate our method on a large subset of datasets in the D4RL benchmark [18] based on the Mu Jo Co simulator [69], including three environments (halfcheetah, hopper, and walker2d) and four dataset types (random, medium, mixed, medium-expert), yielding a total of 12 problem settings. |
| Dataset Splits | No | The paper describes the types of datasets used from the D4RL benchmark (random, medium, mixed, medium-expert) but does not provide specific train/validation/test split percentages or counts for reproduction. |
| Hardware Specification | No | For more details on the experimental set-up and hyperparameters, see Appendix G. |
| Software Dependencies | No | The paper mentions the use of 'Mu Jo Co simulator' but does not provide specific software names with version numbers. |
| Experiment Setup | Yes | For more details on the experimental set-up and hyperparameters, see Appendix G. |