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..
Identifying Selections for Unsupervised Subtask Discovery
Authors: Yiwen Qiu, Yujia Zheng, Kun Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results on a challenging Kitchen environment demonstrate that the learned subtasks effectively enhance the generalization to new tasks in multi-task imitation learning scenarios. |
| Researcher Affiliation | Academia | Yiwen Qiu Carnegie Mellon University Pittsburgh, PA 15213 EMAIL Yujia Zheng Carnegie Mellon University Pittsburgh, PA 15213 EMAIL Kun Zhang Carnegie Mellon University, MBZUAI Pittsburgh, PA 15213 EMAIL |
| Pseudocode | Yes | Algorithm 1 Seq NMF for learning subtasks |
| Open Source Code | Yes | The codes are provided at this link. |
| Open Datasets | Yes | We use the demonstrations provided by (Gupta et al. [2019]) for reproducibility, which only contain state and action pairs but not reward. |
| Dataset Splits | No | The paper only explicitly mentions 'training' and 'testing' sets, but does not provide details for a distinct 'validation' set. |
| Hardware Specification | Yes | All experiments were conducted on either NVIDIA L40, or Ge Force RTX 3080 Ti, or a Mac M1 chip with 16GB of RAM. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | For the experiments in seq-NMF, we use the following hyperparameters in Tab. 4. ... For the experiments in transfering to new tasks, we use the following hyperparameters in Tab. 5. |