Summary of Dourn: Improving Douzero by Residual Neural Networks, By Yiquan Chen et al.
DouRN: Improving DouZero by Residual Neural Networks
by Yiquan Chen, Yingchao Lyu, Di Zhang
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 to deep reinforcement learning in the card game Doudizhu, which combines elements of cooperation and confrontation, is proposed. Building on previous work, such as DouZero, this study incorporates residual networks into the model and explores different architectural designs. The findings demonstrate that this model significantly improves the winning rate within the same training time, consistently outperforming the existing version of DouZero and even experienced human players. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a game where three players combine elements of cooperation and confrontation, deep reinforcement learning can be used to improve winning rates. By incorporating residual networks into the model and testing different designs, this study shows that it’s possible to get better results in less time. This is especially impressive when compared to both an earlier version of the same model and human players. |
Keywords
* Artificial intelligence * Reinforcement learning
