Summary of Generalizing Soft Actor-critic Algorithms to Discrete Action Spaces, by Le Zhang et al.
Generalizing soft actor-critic algorithms to discrete action spaces
by Le Zhang, Yong Gu, Xin Zhao, Yanshuo Zhang, Shu Zhao, Yifei Jin, Xinxin Wu
First submitted to arxiv on: 8 Jul 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 proposed soft actor-critic (SAC) algorithm, a practical discrete variant of SAC, is integrated into the advanced Rainbow variant, known as “bigger, better, faster’’ (BBF). This new combination, called SAC-BBF, improves the previous state-of-the-art interquartile mean (IQM) from 1.045 to 1.088 using only a replay ratio of 2. Additionally, SAC-BBF achieves super-human performance with an IQM greater than one while requiring only one-third of the training time required by BBF. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new algorithm that enables off-policy learning for discrete domains using policy heads in SAC. The proposed SAC-BBF model uses Rainbow as its base and improves upon previous state-of-the-art results. The algorithm is tested on the ATARI suite, a set of video games used to test reinforcement learning algorithms. |
Keywords
» Artificial intelligence » Reinforcement learning
