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.
Learning Abstract Options
Authors: Matthew Riemer, Miao Liu, Gerald Tesauro
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,396 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,185 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 empirical results in both discrete and continuous environments demonstrate the efficiency of our framework. |
| Researcher Affiliation | Industry | Matthew Riemer, Miao Liu, and Gerald Tesauro IBM Research T.J. Watson Research Center, Yorktown Heights, NY EMAIL |
| Pseudocode | No | The paper states: "We include detailed algorithm descriptions for all of our experiments in Appendix 2." However, it does not explicitly contain a clearly labeled "Pseudocode" or "Algorithm" block in the main text, and the content of Appendix 2 is not provided to verify. |
| Open Source Code | No | The paper does not provide any specific repository link, explicit code release statement, or mention of code in supplementary materials for the methodology described. |
| Open Datasets | Yes | We first consider a navigation task in the four-rooms domain [29]. ... We finally consider application of the HOC to the Atari games [2]. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, and test sets. It mentions evaluating policies over certain episodes but not explicit data splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using A3C and various network components (convolutional network, LSTM) but does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | We include detailed algorithm descriptions for all of our experiments in Appendix 2. We also conducted hyperparameter optimization that is summarized along with detail on experimental protocol in Appendix 2. |