Summary of Revisiting Data Augmentation in Deep Reinforcement Learning, by Jianshu Hu et al.
Revisiting Data Augmentation in Deep Reinforcement Learning
by Jianshu Hu, Yunpeng Jiang, Paul Weng
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 proposed study analyzes various data augmentation techniques in image-based deep reinforcement learning (DRL) to determine the optimal approach. The authors express the variance of Q-targets and empirical actor/critic losses to analyze the effects of different components and compare methods. They also formulate an explanation for how data augmentation transformations affect target Q-values, providing recommendations for principled exploitation. Additionally, they incorporate a regularization term called tangent prop, previously used in computer vision but novel in DRL. The study evaluates its proposition and validates analysis across multiple domains, achieving state-of-the-art performance in most environments while demonstrating higher sample efficiency and better generalization ability in complex settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at ways to make deep reinforcement learning (DRL) work better with less data. Researchers have tried different techniques to help DRL learn from smaller datasets, but it’s not clear which one is best. The study takes a closer look at these methods to see how they’re connected and what makes them work or not work. They also add a new idea called tangent prop that helps make the learning process smoother. By testing their ideas on different tasks, they show that their approach can do better than others in many cases. |
Keywords
* Artificial intelligence * Data augmentation * Generalization * Regularization * Reinforcement learning
