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.
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
Authors: Shenao Zhang, Boyi Liu, Zhaoran Wang, Tuo Zhao
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 32,261 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,133 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 | Our experimental results demonstrate that proper normalization significantly reduces the gradient variance of model-based RP PGMs. As a result, the performance of the proposed method is comparable or superior to other gradient estimators, such as the Likelihood Ratio (LR) gradient estimator. Our code is available at https://github.com/agentification/RP_PGM. |
| Researcher Affiliation | Academia | 1Northwestern University 2Georgia Tech |
| Pseudocode | Yes | Algorithm 1 Model-Based Reparameterization Policy Gradient |
| Open Source Code | Yes | Our code is available at https://github.com/agentification/RP_PGM. |
| Open Datasets | No | The paper refers to "Mu Jo Co [57] tasks" as the environment for experiments, but does not provide concrete access information (link, DOI, formal citation with authors/year, or specific dataset name) for a publicly available dataset used for training. |
| Dataset Splits | No | The paper does not specify exact percentages, sample counts, or refer to predefined splits for training, validation, or test datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions using "Py Torch [44]" but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | No | The paper describes network architectures and algorithm choices, but does not provide specific hyperparameters like learning rate, batch size, or number of epochs for the experimental setup. |