Summary of Mastering the Game Of Guandan with Deep Reinforcement Learning and Behavior Regulating, by Yifan Yanggong et al.
Mastering the Game of Guandan with Deep Reinforcement Learning and Behavior Regulating
by Yifan Yanggong, Hao Pan, Lei Wang
First submitted to arxiv on: 21 Feb 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 The proposed GuanZero framework enables AI agents to master the challenging game of Guandan, a decision-making puzzle that even professional human players struggle with. By combining Monte-Carlo methods and deep neural networks, GuanZero regulates the behavior of game-playing agents through a carefully designed neural network encoding scheme. The paper demonstrates the effectiveness of this approach by comparing it with state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Guandan is a game where humans often make mistakes. To help AI agents play better, researchers created a framework called GuanZero. It uses special math and computer learning to decide what moves to make next. This helps the AI agent learn from its mistakes and get better at playing the game. The new approach does better than other ways people have tried to teach AI agents how to play. |
Keywords
* Artificial intelligence * Neural network
