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..
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Authors: Zhecheng Yuan, Zhengrong Xue, Bo Yuan, Xueqian Wang, YI WU, Yang Gao, Huazhe Xu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on DMControl Generalization Benchmark, DMControl Manipulation Tasks, Drawer World, and CARLA to verify the effectiveness of PIE-G. |
| Researcher Affiliation | Academia | Zhecheng Yuan1, Zhengrong Xue2, Bo Yuan3, Xueqian Wang1, Yi Wu1,4, Yang Gao1,4, Huazhe Xu1,4 1 Tsinghua University 2 Shanghai Jiao Tong University 3 Qianyuan Institute of Sciences 4 Shanghai Qi Zhi Institute |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project Page: https://sites.google.com/view/pie-g/home. and Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] In the supplementary material |
| Open Datasets | Yes | DMControl Generalization Benchmark (DMC-GB) [26], DMControl Manipulation Tasks [72], Drawer World [75] that is modified from Meta World [82], and CARLA [13], a realistic autonomous driving simulator. and Image Net pre-trained Res Net model |
| Dataset Splits | No | The paper describes training and evaluation procedures on benchmarks but does not provide explicit training, validation, or test dataset splits (e.g., 80/10/10 percentages or specific sample counts) for reproducibility of data partitioning. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] In the supplementary material |
| Software Dependencies | No | The paper states that training details are in the supplementary material, but it does not explicitly provide specific software dependencies with version numbers in the main text. |
| Experiment Setup | Yes | More training details and environment descriptions are in Appendix B. and Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] In the supplementary material |