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..
Learning World Models with Identifiable Factorization
Authors: Yuren Liu, Biwei Huang, Zhengmao Zhu, Honglong Tian, Mingming Gong, Yang Yu, Kun Zhang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments in synthetic worlds demonstrate that our method accurately identifies the ground-truth latent variables, substantiating our theoretical findings. Moreover, experiments in variants of the Deep Mind Control Suite and Robo Desk showcase the superior performance of our approach over baselines. |
| Researcher Affiliation | Collaboration | 1 National Key Laboratory for Novel Software Technology, Nanjing University, China 2 University of California San Diego, USA 3 Carnegie Mellon University, USA 4 Mohamed bin Zayed University of Artificial Intelligence, UAE 5 University of Melbourne, Australia 6 Polixir.ai, China 7 Peng Cheng Laboratory, China |
| Pseudocode | Yes | Algorithm 1: IFactor |
| Open Source Code | Yes | The source code is available at https://github.com/Alex Liuyuren/IFactor |
| Open Datasets | Yes | Moreover, experiments in variants of the Deep Mind Control Suite and Robo Desk showcase the superior performance of our approach over baselines. |
| Dataset Splits | No | The paper describes training on collected trajectories and evaluating policies, but does not specify explicit train/validation/test dataset splits by percentage or count for fixed datasets. |
| Hardware Specification | Yes | Computing Hardware We used a machine with the following CPU specifications: Intel(R) Xeon(R) Silver 4110 CPU @ 2.10GHz; 32 CPUs, eight physical cores per CPU, a total of 256 logical CPU units. The machine has two Ge Force RTX 2080 Ti GPUs with 11GB GPU memory. |
| Software Dependencies | Yes | The models are implemented in Py Torch 1.13.1. |
| Experiment Setup | Yes | For all experiments, we assign β1 = β2 = β3 = β4 = 0.003 as the weights for the KL divergence terms. (from E.1) and Table 5: Some hyperparameters of our method in the environment of Modified Cartpole, Robodesk and DMC. |