Summary of Disentangling Policy From Offline Task Representation Learning Via Adversarial Data Augmentation, by Chengxing Jia et al.
Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation
by Chengxing Jia, Fuxiang Zhang, Yi-Chen Li, Chen-Xiao Gao, Xu-Hui Liu, Lei Yuan, Zongzhang Zhang, Yang Yu
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Offline meta-reinforcement learning (OMRL) enables agents to tackle novel tasks using static datasets. Existing OMRL research learns separate task representations, incorporating policy input to form context-based meta-policies. Contrastive learning with multi-task offline data is a common approach for training task representations, leveraging interactions from various policies. However, collecting data from numerous policies is impractical and often unattainable in realistic settings. Our proposed algorithm introduces adversarial data augmentation to disentangle behavior policy effects from task representation learning. This process generates samples designed to confound learned task representations and improve out-of-distribution generalization. Our experiments demonstrate the effectiveness of this approach, achieving robust and satisfactory task identification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching computers to learn new things without needing lots of data or practice. Right now, computers can only learn by trying many different actions and seeing what happens. But humans can learn from just a few examples! The researchers in this paper want to help computers do the same thing. They created a special way for computers to learn about new tasks using just some examples, without needing to try everything. This is important because it will make computers smarter and more helpful. |
Keywords
* Artificial intelligence * Data augmentation * Generalization * Multi task * Reinforcement learning * Representation learning
