Summary of How to Solve Contextual Goal-oriented Problems with Offline Datasets?, by Ying Fan et al.
How to Solve Contextual Goal-Oriented Problems with Offline Datasets?
by Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, Ching-An Cheng
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes Contextual goal-Oriented Data Augmentation (CODA), a novel method for solving contextual goal-oriented (CGO) problems. CODA utilizes unlabeled trajectories and context-goal pairs, leveraging an action-augmented Markov decision process (MDP) to create a fully labeled transition dataset without introducing approximation errors. Theoretical analysis demonstrates the approach’s effectiveness in offline data settings, while empirical results showcase improved performance compared to baseline methods across various CGO problem scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to solve problems where you need to make decisions based on goals and context. It uses information from unlabeled data and pairs of context and goals to create labeled data that can be used for training. This approach is tested and shown to be effective in solving these types of problems, which is important because it could help us learn more about how to make decisions in different situations. |
Keywords
» Artificial intelligence » Data augmentation
