Summary of Goal-conditioned Data Augmentation For Offline Reinforcement Learning, by Xingshuai Huang et al.
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning
by Xingshuai Huang, Di Wu Member, Benoit Boulet
First submitted to arxiv on: 29 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 The paper proposes Goal-cOnditioned Data Augmentation (GODA), a novel method for augmenting offline reinforcement learning datasets with higher-quality samples. GODA uses generative modeling techniques, including a return-oriented goal condition and controllable scaling, to generate new data that is selectively more relevant to the original dataset’s optimal demonstrations. The approach aims to improve the quality of offline RL datasets by leveraging limited optimal demonstrations. Experimental results on the D4RL benchmark and real-world traffic signal control tasks demonstrate GODA’s effectiveness in enhancing data quality and outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GODA is a new way to make offline reinforcement learning better. Offline RL means we can learn from old data without needing to interact with the environment anymore. The problem is that this data might not be very good, which makes it hard for our models to learn well. GODA tries to fix this by adding new, higher-quality data points to the mix. It does this using special algorithms and mathematical tricks. This helps the model learn more effectively from the limited optimal demonstrations we have. The results show that GODA is better than other methods at making the most of the data we have. |
Keywords
» Artificial intelligence » Data augmentation » Reinforcement learning
