Summary of Efficient Learning in Chinese Checkers: Comparing Parameter Sharing in Multi-agent Reinforcement Learning, by Noah Adhikari and Allen Gu
Efficient Learning in Chinese Checkers: Comparing Parameter Sharing in Multi-Agent Reinforcement Learning
by Noah Adhikari, Allen Gu
First submitted to arxiv on: 29 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper explores the application of multi-agent reinforcement learning (MARL) in the competitive perfect-information homogenous game of Chinese Checkers. The authors develop a new MARL environment, variable-size, six-player Chinese Checkers, which supports traditional rules including chaining jumps. They demonstrate that MARL with full parameter sharing outperforms independent and partially shared architectures. This achievement is significant as it marks the first implementation of Chinese Checkers that remains faithful to the true game. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Chinese Checkers is a classic board game where players move pieces to capture their opponent’s pieces. Researchers used artificial intelligence (AI) to create a new version of the game called variable-size, six-player Chinese Checkers. This AI game is different because it has six players and allows for bigger moves. The researchers also showed that this AI game helps machines learn better than when each machine learns alone. |
Keywords
» Artificial intelligence » Reinforcement learning
