Summary of Corrected Soft Actor Critic For Continuous Control, by Yanjun Chen et al.
Corrected Soft Actor Critic for Continuous Control
by Yanjun Chen, Xinming Zhang, Xianghui Wang, Zhiqiang Xu, Xiaoyu Shen, Wei Zhang
First submitted to arxiv on: 22 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 The Soft Actor-Critic (SAC) algorithm is a popular choice for deep reinforcement learning due to its stability and high sample efficiency. However, the tanh transformation applied to sampled actions can distort the action distribution, making it harder to select the most probable actions. This paper proposes a novel approach that directly identifies and selects the most likely actions within the transformed distribution, addressing this issue. The method is evaluated on standard continuous control benchmarks, demonstrating significant performance enhancements compared to the original SAC algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves the Soft Actor-Critic (SAC) algorithm for deep reinforcement learning. SAC is good because it’s stable and efficient. But there was a problem: the way it picked actions didn’t always choose the best one. The new method fixes this by finding the most likely action right away. It tests this new method on standard challenges and shows that it works better than before. |
Keywords
» Artificial intelligence » Reinforcement learning » Tanh
