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..
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning
Authors: Seungyong Moon, Junyoung Yeom, Bumsoo Park, Hyun Oh Song
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments |
| Researcher Affiliation | Collaboration | Seungyong Moon1, Junyoung Yeom1, Bumsoo Park2, Hyun Oh Song1 1Seoul National University, 2KRAFTON |
| Pseudocode | Yes | Algorithm 1 PPO with achievement distillation |
| Open Source Code | Yes | The code can be found at https://github.com/snu-mllab/Achievement-Distillation. |
| Open Datasets | Yes | We primarily utilize the Crafter environment as a benchmark to assess the capabilities of an agent in solving MDPs with hierarchical achievements [19]. |
| Dataset Splits | No | The paper uses a procedurally generated environment (Crafter) where data is collected through interaction, rather than specifying static train/validation/test dataset splits. |
| Hardware Specification | No | No specific hardware details such as GPU or CPU models, or cloud computing instance types, were provided for the experiments. |
| Software Dependencies | No | No specific software versions for libraries or frameworks (e.g., PyTorch, TensorFlow, Python) were explicitly listed in the paper. |
| Experiment Setup | Yes | We provide the implementation details and hyperparameters in Appendix C. |