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].
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
Authors: Seungyul Han, Youngchul Sung
ICML 2019 | Venue PDF | LLM Run Details
| 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. |