Object-Category Aware Reinforcement Learning
Authors: Qi Yi, Rui Zhang, shaohui peng, Jiaming Guo, Xing Hu, Zidong Du, xishan zhang, Qi Guo, Yunji Chen
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that OCARL can improve both the sample efficiency and generalization in the OORL domain. |
| Researcher Affiliation | Collaboration | 1University of Science and Technology of China 2SKL of Processors, Institute of Computing Technology, CAS 3Cambricon Technologies 4University of Chinese Academy of Sciences |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 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] |
| Open Datasets | Yes | The images to render these objects come from Crafter [13], which is under MIT license. |
| Dataset Splits | No | The paper describes training agents on certain Hunter environments (Hunter-Z1C0/Z0C1, Hunter-Z1C1, Hunter-Z4C0/Z0C4) and testing on another (Hunter-Z4C4), but does not explicitly define training/validation/test dataset splits with percentages or counts for model development. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] |
| Software Dependencies | No | The paper mentions several software frameworks and algorithms like PPO, RRL, SMORL, and SPACE, but it does not specify version numbers for these or other ancillary software dependencies (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] see Appendix. For more implementation details, please refer to the Appendix. |