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 [1].
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. |