Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 17,159 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,690 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |