Generalized Value Iteration Networks:Life Beyond Lattices
Authors: Sufeng Niu, Siheng Chen, Hanyu Guo, Colin Targonski, Melissa Smith, Jelena Kovačević
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |