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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Generalized Value Iteration Networks:Life Beyond Lattices
Authors: Sufeng Niu, Siheng Chen, Hanyu Guo, Colin Targonski, Melissa Smith, Jelena Kovačević
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through intensive experiments we demonstrate the generalization ability of GVIN within imitation learning and episodic Q-learning for various datasets, including synthetic 2D maze data, irregular graphs, and real-world maps (Minnesota highway and New York street maps); we show that GVIN significantly outperforms VIN with discretization input on irregular structures; See Section Experimental Results. |
| Researcher Affiliation | Collaboration | Clemson University, 433 Calhoun Dr., Clemson, SC 29634, USA Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA Uber Advanced Technologies Group, 100 32nd St, Pittsburgh, PA 15201, USA |
| Pseudocode | Yes | The pseudocode for the algorithm is presented in Supplementary. |
| Open Source Code | No | The paper provides a footnote linking to the VIN project's GitHub (1https://github.com/avivt/VIN) for generating 2D mazes, which is a baseline, but does not provide a link or explicit statement about releasing the source code for their proposed GVIN methodology. |
| Open Datasets | Yes | We generate 22, 467 2D mazes (16 16) using the same scripts1 that VIN used. |
| Dataset Splits | No | The paper states "6/7 data for training and 1/7 data for testing" but does not explicitly mention a separate validation split or subset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Additional experiment parameter settings are listed in the Supplementary. [...] We set recurrence parameter to K = 200. |