Summary of Transformer Based Planning in the Observation Space with Applications to Trick Taking Card Games, by Douglas Rebstock et al.
Transformer Based Planning in the Observation Space with Applications to Trick Taking Card Games
by Douglas Rebstock, Christopher Solinas, Nathan R. Sturtevant, Michael Buro
First submitted to arxiv on: 19 Apr 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 This research paper proposes a novel approach to search algorithms for imperfect information games with an extremely large number of possible underlying states and trajectories. The focus is on trick-taking card games, where traditional search methods struggle. While existing techniques like Perfect Information Monte Carlo (PIMC) search have shown promise, they still face significant limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a trick-taking card game, you can’t see all the cards or know exactly what will happen next. Computers struggle to find the best moves because there are so many possibilities. Researchers tried using techniques called state sampling, but even those had big problems. They want to make better algorithms that can figure out good moves in these tricky games. |
