How to Solve Contextual Goal-Oriented Problems with Offline Datasets?

Abstract

We present a novel method, Contextual goal-Oriented Data Augmentation (CODA), which uses commonly available unlabeled trajectories and context-goal pairs to solve Contextual Goal-Oriented (CGO) problems. By carefully constructing an action-augmented MDP that is equivalent to the original MDP, CODA creates a fully labeled transition dataset under training contexts without additional approximation error. We conduct a novel theoretical analysis to demonstrate CODA's capability to solve CGO problems in the offline data setup. Empirical results also showcase the effectiveness of CODA, which outperforms other baseline methods across various context-goal relationships of CGO problem. This approach offers a promising direction to solving CGO problems using offline datasets.

PDF NeurIPS OpenReview Semantic Scholar

Cite

Text

Fan et al. "How to Solve Contextual Goal-Oriented Problems with Offline Datasets?." Neural Information Processing Systems, 2024. doi:10.52202/079017-3155

Markdown

[Fan et al. "How to Solve Contextual Goal-Oriented Problems with Offline Datasets?." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/fan2024neurips-solve/) doi:10.52202/079017-3155

BibTeX

@inproceedings{fan2024neurips-solve,
  title     = {{How to Solve Contextual Goal-Oriented Problems with Offline Datasets?}},
  author    = {Fan, Ying and Li, Jingling and Swaminathan, Adith and Modi, Aditya and Cheng, Ching-An},
  booktitle = {Neural Information Processing Systems},
  year      = {2024},
  doi       = {10.52202/079017-3155},
  url       = {https://mlanthology.org/neurips/2024/fan2024neurips-solve/}
}