SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions
Authors: Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen, Roberto Martín-Martín, Amy Zhang, Scott Niekum, Peter Stone
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Ski LD in several domains with challenging, long-horizon sparse reward tasks including a realistic simulated household robot domain, where Ski LD successfully learns skills with clear semantic meaning and shows superior performance compared to existing unsupervised reinforcement learning methods that only maximize state coverage. |
| Researcher Affiliation | Collaboration | 1University of Texas at Austin 2University of Massachusettes, Amherst 3Sony AI |
| Pseudocode | Yes | Algorithm 1 Ski LD Skill Discovery |
| Open Source Code | Yes | Code and visualizations are at https://wangzizhao.github.io/Ski LD/. |
| Open Datasets | Yes | We evaluate our method on two challenging object-rich embodied AI benchmarks: Mini-behavior [32] and Interactive Gibson [42]. |
| Dataset Splits | No | The paper describes pre-training and finetuning steps, but does not explicitly mention a 'validation' dataset split in the context of fixed datasets or provide specific details for such a split beyond training and evaluation phases. |
| Hardware Specification | Yes | Nvidia A40 GPU; Intel(R) Xeon(R) Gold 6342 CPU @2.80GHz Nvidia A100 GPU; Intel(R) Xeon(R) Gold 6342 CPU @2.80GHz |
| Software Dependencies | No | The codebase is built on tianshou [64] for backend RL, though with significant modifications. |
| Experiment Setup | Yes | The hyperparameters of skill learning and task learning can be found in Table 1. |