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..
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
Authors: Yunfan Li, Yiran Wang, Yu Cheng, Lin Yang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, we empirically test our theory with deep neural nets to show the benefits of the theoretical inspiration. |
| Researcher Affiliation | Collaboration | 1Department of Electical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA 2Microsoft Research, Redmond, WA, USA. |
| Pseudocode | Yes | Algorithm 1 LPO; Algorithm 2 LPO (Practical Implementation); Algorithm 3 S-Sampling (Sensitivity-Sampling); Algorithm 4 Policy Update; Algorithm 5 Behaviour Policy Sampling; Algorithm 6 Policy Evaluation Oracle; Algorithm 7 d-sampler |
| Open Source Code | No | The paper states: "We implemented our method based on the open source package (Raffin et al., 2021)", indicating they used an existing open-source framework (Stable-Baselines3) for their implementation, but they do not explicitly state that the source code for their *own* specific methodology (LPO) is publicly available or provide a link to it. |
| Open Datasets | Yes | To further illustrate the effectiveness of our width function and our proposed sensitivity sampling, we compare (Schulman et al., 2017; Feng et al., 2021) with our proposed LPO in sparse reward Mu Jo Co environments (Todorov et al., 2012). |
| Dataset Splits | No | The paper uses the MuJoCo environments but does not explicitly state specific training, validation, and test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper states: "We implemented our method based on the open source package (Raffin et al., 2021)". While this refers to Stable-Baselines3, it does not specify its version number or any other software dependencies with version numbers, which is necessary for reproducibility. |
| Experiment Setup | Yes | The detailed hyperparameters are showed in the table G. Hyperparameter Value (LPO, ENIAC) Value (PPO) N 2048 2048 T 2e6 2e6 λ 0.95 0.95 γ(int) 0.999 γ(ext) 0.99 0.99 α 2 β 1 Learning rate 1e-4 1e-4 Batch size 32, 16 32, 16 Number of epoch per iteration 10 10 |