Domain Adaptive Imitation Learning
Authors: Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate GAMA against baselines in embodiment, viewpoint, and dynamics mismatch scenarios where aligned demonstrations don t exist and show the effectiveness of our approach. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University 2Department of Computer Science, Tsinghua University. |
| Pseudocode | Yes | Algorithm 1 Generative Adversarial MDP Alignment (GAMA) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code for the methodology described, nor does it include a link to a code repository. |
| Open Datasets | Yes | We experiment with environments which are extensions of Open AI Gym (Brockman et al., 2016): pen, cart, reacher2, reacher3, reach2-tp, snake3, and snake4 denotes the pendulum, cartpole, 2-link reacher, 3link reacher, third person 2-link reacher, 3-link snake, and 4-link snake environments, respectively. |
| Dataset Splits | No | The paper describes using an "alignment task set" and "target task" but does not provide specific numerical train/validation/test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the implementation of the experiments. |
| Experiment Setup | No | The paper states that 'Model architectures and environment details are further described Appendix B, C, D.' but the main text itself does not provide specific hyperparameter values or detailed training configurations for reproducibility. |