Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search
Authors: Shinsaku Sakaue, Taihei Oki
NeurIPS 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theoretical paper and no negative societal impacts are expected. |
| Researcher Affiliation | Academia | Shinsaku Sakaue The University of Tokyo Tokyo, Japan EMAIL Taihei Oki The University of Tokyo Tokyo, Japan EMAIL |
| Pseudocode | Yes | Algorithm 1 GBFS with heuristic function values ρ Algorithm 2 A* with heuristic function values ρ |
| Open Source Code | No | The reproducibility checklist states: "(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]" |
| Open Datasets | No | The paper is theoretical and does not use or provide access information for a specific public dataset. It mentions: "assuming that path-finding instances defined on a fixed vertex set of size n are drawn i.i.d. from an unknown distribution." |
| Dataset Splits | No | The paper is theoretical and does not describe experimental data splits (training, validation, test). The reproducibility checklist states: "(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]" |
| Hardware Specification | No | The paper is theoretical and does not involve empirical experiments requiring hardware specifications. The reproducibility checklist states: "(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]" |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers for empirical experiments. The reproducibility checklist states: "(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]" |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. The reproducibility checklist states: "(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]" |