Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
Authors: Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris M. Kitani
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our approach, Transform2Act, outperforms prior methods significantly in terms of convergence speed and final performance. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University, 2Cornell University {yyuan2,yudas,zluo2,kkitani}@cs.cmu.edu, ws455@cornell.edu |
| Pseudocode | Yes | An overview of our method is provided in Figure 2. We also outline our approach in Algorithm 1. |
| Open Source Code | Yes | Code and videos are available at https://sites.google.com/view/transform2act. |
| Open Datasets | No | The paper uses simulation environments (Mu Jo Co simulator) for experiments rather than external, publicly available datasets. Therefore, there is no dataset for which to provide concrete access information. |
| Dataset Splits | No | The paper uses a simulation environment, and while it discusses training, it does not specify traditional dataset splits (e.g., percentages or counts for training, validation, and test sets) that would be applicable to a fixed dataset. Instead, it refers to batch sizes and training epochs within the reinforcement learning paradigm. |
| Hardware Specification | Yes | For all the environments used in the paper, it takes around one day to train our model on a standard server with 20 CPU cores and an NVIDIA RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | Yes | In this section, we present the hyperparameters searched and used for our method in Table 1 and the hyperparameters for the baselines in Table 2. |