Arbitrarily Scalable Environment Generators via Neural Cellular Automata
Authors: Yulun Zhang, Matthew Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental Evaluation |
| Researcher Affiliation | Academia | Yulun Zhang1 Matthew C. Fontaine2 Varun Bhatt2 Stefanos Nikolaidis2 Jiaoyang Li1 1Robotics Institute, Carnegie Mellon University 2Thomas Lord Department of Computer Science, University of Southern California yulunzhang@cmu.edu, {mfontaine,vsbhatt,nikolaid}@usc.edu, jiaoyangli@cmu.edu |
| Pseudocode | No | The paper describes methods and processes but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | We include the source code at https://github.com/lunjohnzhang/warehouse_ env_gen_nca_public. |
| Open Datasets | No | The paper describes various 'domains' (warehouse, manufacturing, maze) for which environments are generated or used in simulations. While some domains refer to previous works (e.g., [28, 51] for warehouse, [16, 5, 1] for maze), no specific links, DOIs, or explicit statements for public access to the *datasets* used for training or evaluation, or specific author/year citations for these datasets, are provided. For the manufacturing domain, it states, 'The manufacturing domain is new, so we create human-designed environments for it,' but does not provide access. |
| Dataset Splits | No | The paper discusses training and evaluation of NCA generators and agent-based simulations (e.g., 'during training' and 'during evaluation' in Section 6, and 'Table 1: Summary of the experiment setup' with S and Seval), but it does not specify any dataset splits like train/validation/test percentages or counts for reproducing data partitioning. |
| Hardware Specification | No | Appendix C.3 states, 'This work used Bridge-2 at Pittsburgh Supercomputing Center (PSC) through allocation CIS220115 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program', but it does not provide specific hardware details such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | Appendix C.4 states 'Our implementation uses Python 3.9 and PyTorch [38] with an NVIDIA GPU.' and references 'IBM ILOG CPLEX Optimization Studio [21]', but it does not provide specific version numbers for PyTorch, CPLEX, or other key libraries beyond Python 3.9. |
| Experiment Setup | Yes | Table 1 'Summary of the experiment setup' provides specific details such as environment sizes (S=36x33, Seval=101x102), number of agents (Na=200, Na_eval=1400/2250), number of simulations (Ne=5, Neval=10000/100000), NCA iterations (C=50, Ceval=200), and timesteps (T=1000, Teval=5000). Section 6 also discusses hyperparameters like the value in fopt. |