Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Authors: Chao Yang, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu, Junzhou Huang, Chuang Gan
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Considerable empirical results on challenging benchmarks indicate that our method attains consistent improvements over other Lf O counterparts. |
| Researcher Affiliation | Collaboration | 1 Beijing National Research Center for Information Science and Technology (BNRist), State Key Lab on Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University 2 Center for Vision, Cognition, Learning and Autonomy, Department of Computer Science, UCLA 3 Tencent AI Lab, 4 MIT-IBM Watson AI Lab |
| Pseudocode | Yes | Algorithm 1 Inverse-Dynamics-Disagreement-Minimization (IDDM) |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | To verify the effectiveness of our IDDM, we perform experimental comparisons on seven challenging control tasks, ranging from traditional control to locomotion [8]. |
| Dataset Splits | No | The paper mentions evaluating |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using "PPO [32]" and "MINE [4, 14]" but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | Due to the space limit, we defer more detailed speciļ¬cations of all the evaluated tasks into the supplementary material. |