Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design

Authors: Andrew Wagenmaker, Kevin G. Jamieson

NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as a URL)? [N/A] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] (c) Did you report error bars (e.g., with respect to the random seed after running experi- ments multiple times)? [N/A] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]
Researcher Affiliation Academia Andrew Wagenmaker & Kevin Jamieson Paul G. Allen School of Computer Science & Engineering University of Washington Seattle, WA 98195
Pseudocode Yes Algorithm 1 Policy Learning via Experiment Design in Linear MDPs (PEDEL)
Open Source Code No 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as a URL)? [N/A]
Open Datasets No The paper is theoretical and does not mention using any public or open datasets for training. The ethics review checklist indicates N/A for experimental details.
Dataset Splits No The paper does not specify training, validation, or test dataset splits. The ethics review checklist indicates N/A for training details.
Hardware Specification No The paper does not explicitly describe the hardware used for experiments. The ethics review checklist indicates N/A for compute resources.
Software Dependencies No The paper does not provide specific software dependencies with version numbers. The ethics review checklist indicates N/A for training details.
Experiment Setup No The paper does not provide specific experimental setup details, such as hyperparameter values or training configurations. The ethics review checklist indicates N/A for training details.