Summary of Doubly Mild Generalization For Offline Reinforcement Learning, by Yixiu Mao et al.
Doubly Mild Generalization for Offline Reinforcement Learning
by Yixiu Mao, Qi Wang, Yun Qu, Yuhang Jiang, Xiangyang Ji
First submitted to arxiv on: 12 Nov 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 This research paper explores the challenges of Offline Reinforcement Learning (RL) due to extrapolation error and value overestimation. The authors identify that recent approaches have focused on in-sample learning, but this overlooks the potential benefits of mild generalization beyond the dataset. To address this, they propose Doubly Mild Generalization (DMG), which consists of mild action generalization and mild generalization propagation. DMG ensures better performance than the in-sample optimal policy in certain scenarios and can control value overestimation. Empirically, DMG achieves state-of-the-art performance across various locomotion tasks and AntMaze tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline Reinforcement Learning is like trying to learn new skills without actually practicing them. The problem is that this approach doesn’t account for situations where we need to generalize what we’ve learned to new situations. Researchers have found ways to improve this process, but they still struggle with overestimating the value of certain actions. To solve this issue, scientists propose a new method called Doubly Mild Generalization. This technique involves learning from small changes in our actions and making sure that these changes don’t get out of control. The results show that this approach can lead to better performance than just relying on what we’ve learned so far. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
