Notice: 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. Full accuracy metrics and methodology are described in [1].
Improving PAC Exploration Using the Median Of Means
Authors: Jason Pazis, Ronald E. Parr, Jonathan P. How
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compared Median-PAC against the algorithm of Pazis and Parr [12] on a simple 5 by 5 gridworld (see appendix for more details). |
| Researcher Affiliation | Academia | Jason Pazis Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge, MA 02139, USA EMAIL Ronald Parr Department of Computer Science Duke University Durham, NC 27708 EMAIL Jonathan P. How Aerospace Controls Laboratory Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge, MA 02139, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Median-PAC |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | No | The experiments are conducted in a 'simple 5 by 5 gridworld' which describes the environment, not a publicly available dataset. There is no information about accessing a pre-existing dataset. |
| Dataset Splits | No | The paper describes experiments in a 'simple 5 by 5 gridworld' environment, which is an interaction-based setup, and does not mention any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | While the paper describes the environment and the algorithm's inputs (parameters k, km, a), it does not provide specific numerical values for hyperparameters or training settings within the main text or the provided abstract/introduction/experimental section. |