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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
Authors: Seungyul Han, Youngchul Sung
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 19,420 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,119 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 | Numerical results show that the proposed new algorithm outperforms PPO and other RL algorithms in various Open AI Gym tasks. |
| Researcher Affiliation | Academia | 1School of Electrical Engineering, KAIST, Daejeon, South Korea. Correspondence to: Youngchul Sung <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 DISC |
| Open Source Code | Yes | The source code for DISC is available at http://github.com/seungyulhan/disc/. |
| Open Datasets | Yes | We evaluate our algorithm on various Open AI GYM tasks (Brockman et al., 2016) |
| Dataset Splits | No | The paper discusses training on environments and reusing old sample batches but does not explicitly provide training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using Open AI Gym tasks and baselines but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | Detailed description of the hyper-parameters of PPO, PPOAMBER and DISC is provided in Table A.1 in Appendix. |