Learning to Discover Skills through Guidance
Authors: HYUNSEUNG KIM, BYUNG KUN LEE, Hojoon Lee, Dongyoon Hwang, Sejik Park, Kyushik Min, Jaegul Choo
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation demonstrates that DISCO-DANCE outperforms other USD baselines in challenging environments, including two navigation benchmarks and a continuous control benchmark. |
| Researcher Affiliation | Collaboration | 1Kim Jaechul Graduate School of AI, KAIST. 2KAKAO Corp. |
| Pseudocode | Yes | Algorithm 1: Skill Discovery through Guidance |
| Open Source Code | Yes | Qualitative visualizations and code of DISCO-DANCE are available at https://mynsng.github.io/discodance/. |
| Open Datasets | Yes | 2D mazes: We used the publicly available environment code in [7]. Ant mazes: We used the publicly available environment code in [8]. Deepmind Control suite: We used the publicly available environment code in [22, 53]. |
| Dataset Splits | No | The paper describes training steps and finetuning steps in RL environments, but does not specify distinct training, validation, and test *dataset splits* with percentages or sample counts. |
| Hardware Specification | Yes | We conducted all experiments using a single RTX 3090 GPU, and each experiment requires approximately up to 2GB of memory. |
| Software Dependencies | No | The paper mentions software like SAC, DIAYN, APS, SMM, and URLB, and specific environment code, but does not provide specific version numbers for these software components or programming languages. |
| Experiment Setup | Yes | Table 4: A common set of hyperparameters used in DISCO-DANCE. Table 5: Per environment sets of hyperparameters used in DISCO-DANCE. |