Identifying Selections for Unsupervised Subtask Discovery
Authors: Yiwen Qiu, Yujia Zheng, Kun Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 yiwenq@andrew.cmu.edu Yujia Zheng Carnegie Mellon University Pittsburgh, PA 15213 yujiazh2@andrew.cmu.edu Kun Zhang Carnegie Mellon University, MBZUAI Pittsburgh, PA 15213 kunz1@andrew.cmu.edu |
| 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. |