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..
Outcome-directed Reinforcement Learning by Uncertainty \& Temporal Distance-Aware Curriculum Goal Generation
Authors: Daesol Cho, Seungjae Lee, H. Jin Kim
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our algorithm significantly outperforms these prior methods in a variety of challenging navigation tasks and robotic manipulation tasks in a quantitative and qualitative way.1...5 EXPERIMENTS We include 6 environments to validate our proposed method...5.1 EXPERIMENTAL RESULTS...5.2 ABLATION STUDY |
| Researcher Affiliation | Academia | Daesol Cho , Seungjae Lee , H. Jin Kim Seoul National University, Automation and Systems Research Institute (ASRI), Artificial Intelligence Institute of Seoul National University (AIIS) EMAIL |
| Pseudocode | Yes | The overall training process is summarized in Algorithm 1 in Appendix B. Algorithm 2 Meta-NML (Li et al., 2021) |
| Open Source Code | Yes | 1Code is available : https://github.com/jayLEE0301/outpace_official |
| Open Datasets | Yes | We referred to the metaworld (Yu et al., 2020) and EARL (Sharma et al., 2021) environments. |
| Dataset Splits | No | The paper provides 'Meta-learner train sample size' and 'Meta-learner test sample size' for a component of the method, but it does not specify explicit training/validation/test splits (e.g., percentages or counts) for the overall dataset used in the RL experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch (Paszke et al., 2019)' but does not specify a version number for PyTorch or any other software dependencies with their versions. |
| Experiment Setup | Yes | Table 2: Hyperparameters for OUTPACE...Table 3: Env-specific hyperparameters for OUTPACE |