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.
Constrained GPI for Zero-Shot Transfer in Reinforcement Learning
Authors: Jaekyeom Kim, Seohong Park, Gunhee Kim
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,567 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,961 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 | Through experiments in the Scavenger and Reacher environment with state observations as well as the Deep Mind Lab environment with visual observations, we show that the proposed constrained GPI significantly outperforms the prior GPI s transfer performance. |
| Researcher Affiliation | Academia | Jaekyeom Kim Seoul National University EMAIL Seohong Park University of California, Berkeley EMAIL Gunhee Kim Seoul National University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and additional information are available at https://jaekyeom.github.io/projects/cgpi/. |
| Open Datasets | Yes | We start our experiments in the Scavenger environment [8, 9],... and the Deep Mind Lab environment [7, 10, 12]. |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific details on dataset splits for training, validation, and testing (e.g., percentages or sample counts for each split). |
| Hardware Specification | No | The paper states: "Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] We will include in the supplementary material." This information is not present in the main paper. |
| Software Dependencies | No | The paper mentions Pytorch [27] and Adam [21] but does not specify their version numbers or other software dependencies with specific versions. |
| Experiment Setup | No | The paper states: "Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] We will include in the supplementary material." This information is not present in the main paper. |