Summary of Symmetric Q-learning: Reducing Skewness Of Bellman Error in Online Reinforcement Learning, by Motoki Omura et al.
Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning
by Motoki Omura, Takayuki Osa, Yusuke Mukuta, Tatsuya Harada
First submitted to arxiv on: 12 Mar 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 A novel approach, Symmetric Q-learning, is introduced to address the issue of skewed error distributions in deep reinforcement learning. By adding synthetic noise to target values, the method generates a Gaussian error distribution, enabling more effective training of value functions. The proposed method demonstrates improved sample efficiency on continuous control benchmark tasks in MuJoCo, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to learn is discovered! In reinforcement learning, making good choices depends on knowing how good or bad each choice might be. Usually, we use a special formula called the least squares method to figure this out. But sometimes this method doesn’t work well because it assumes that mistakes are random and follow a certain pattern. This paper shows that these mistakes often don’t follow that pattern and can be very different from what’s expected. To fix this problem, the authors created a new way of learning called Symmetric Q-learning. It adds some extra noise to help the learning process work better. The result is that this method learns faster and more efficiently than others. |
Keywords
* Artificial intelligence * Reinforcement learning
