Finding Options that Minimize Planning Time
Authors: Yuu Jinnai, David Abel, David Hershkowitz, Michael Littman, George Konidaris
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate the performance of two heuristic approaches for option discovery, betweenness options (S ims ek & Barto, 2009) and Eigenoptions (Machado et al., 2017), against the proposed approximation algorithms and the optimal options in standard grid domains. |
| Researcher Affiliation | Academia | Yuu Jinnai 1 David Abel 1 D Ellis Hershkowitz 2 Michael L. Littman 1 George Konidaris 1 1Brown University, Providence, RI, United States 2Carnegie Mellon University, Pittsburgh, PA, United States. Correspondence to: Yuu Jinnai <yuu jinnai@brown.edu>. |
| Pseudocode | No | The paper describes algorithms (A-MOMI, A-MIMO) but does not provide them in a formal pseudocode block. |
| Open Source Code | No | The paper does not provide explicit statements about the release of source code or links to a repository for the described methodology. |
| Open Datasets | No | The paper mentions "an 11 x 11 four-room domain and a 9 x 9 grid world" which are standard experimental setups but does not cite a specific public dataset or provide access details. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. It describes grid-based domains without specifying how data was partitioned. |
| Hardware Specification | No | The paper does not specify the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the grid domains and the goal but does not provide specific experimental setup details such as hyperparameters or system-level training settings. |