Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion
Authors: Yongyuan Liang, Tingqiang Xu, Kaizhe Hu, Guangqi Jiang, Furong Huang, Huazhe Xu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate Make-An-Agent, answering the following problems: How does our method compare with other multi-task or learning-to-learn approaches for policy learning, in terms of performance on seen tasks and generalization to unseen tasks? How scalable is our method, and can it be fine-tuned across different domains? Does our method merely memorize policy parameters and trajectories of each task, or can it generate diverse and new behaviors? |
| Researcher Affiliation | Academia | 1 Shanghai Qi Zhi Institute 2 University of Maryland, College Park 3 Tsinghua University 4 University of California, San Diego |
| Pseudocode | No | The paper describes the methodology with equations and conceptual diagrams but does not provide a formally labeled pseudocode or algorithm block. |
| Open Source Code | Yes | i Code, dataset and video are released in https://cheryyunl.github.io/make-an-agent/. |
| Open Datasets | Yes | i Code, dataset and video are released in https://cheryyunl.github.io/make-an-agent/. |
| Dataset Splits | No | The paper describes training and testing procedures but does not explicitly define a 'validation' dataset split for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | All model training are conducted on NVIDIA A40 GPUs. |
| Software Dependencies | No | The paper mentions common deep learning frameworks and algorithms but does not specify exact version numbers for software dependencies (e.g., Python, PyTorch). |
| Experiment Setup | Yes | Table 1: Hyperparameters for Autoencoder; Table 2: Hyperparameters for Behavior Embedding; Table 3: Hyperparameters for Diffusion Model. |