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 | Conference PDF | Archive PDF | Plain Text | 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 |