Cross-Domain Policy Adaptation by Capturing Representation Mismatch
Authors: Jiafei Lyu, Chenjia Bai, Jing-Wen Yang, Zongqing Lu, Xiu Li
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on environments with kinematic and morphology mismatch, and the results show that our method exhibits strong performance on many tasks. |
| Researcher Affiliation | Collaboration | 1Tsinghua Shenzhen International Graduate School, Tsinghua University 2Shanghai Artificial Intelligence Laboratory 3Tencent IEG 4School of Computer Science, Peking University 5Beijing Academy of Artificial Intelligence. |
| Pseudocode | Yes | Algorithm 1 PAR (Abstracted Version) |
| Open Source Code | Yes | Our code is publicly available at https://github.com/dmksjfl/PAR. |
| Open Datasets | Yes | We use four environments (halfcheetah, hopper, walker, ant) from Open AI Gym (Brockman et al., 2016) as source domains |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, such as percentages or sample counts for each split, or reference predefined splits with citations for reproducibility. |
| Hardware Specification | Yes | CPU AMD EPYC 7452 GPU RTX3090 8 Memory 288GB |
| Software Dependencies | No | The paper mentions software like Open AI Gym, D4RL, SAC, DARC, VGDF, CQL, and the Adam optimizer, but does not provide specific version numbers for these software components or libraries. |
| Experiment Setup | Yes | Table 2. Detailed hyperparameter setup for PAR and baseline methods on the evaluated tasks. |