Summary of Reinforcement Learning with Euclidean Data Augmentation For State-based Continuous Control, by Jinzhu Luo et al.
Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control
by Jinzhu Luo, Dingyang Chen, Qi Zhang
First submitted to arxiv on: 16 Oct 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 paper explores data augmentation techniques for reinforcement learning (RL) agents in continuous control tasks. Specifically, it focuses on state-based control, where the agent directly observes kinematic and task features, rather than images with perturbations. The authors propose an alternative data augmentation strategy based on Euclidean symmetries under transformations like rotations, which provides rich augmented data for RL training. This approach significantly improves both data efficiency and asymptotic performance of RL on a wide range of tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a type of artificial intelligence that helps robots learn to do new things. Imagine you’re playing a game where you have to make the right moves to get a reward. This paper is about making this process more efficient by creating fake data that’s similar to real data. It’s like taking a photo and then copying it with some changes, but instead of images, they’re changing the way robots understand their surroundings. The authors show that this new approach makes learning faster and better for many types of tasks. |
Keywords
» Artificial intelligence » Data augmentation » Reinforcement learning
