Summary of Transformers As Game Players: Provable In-context Game-playing Capabilities Of Pre-trained Models, by Chengshuai Shi et al.
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models
by Chengshuai Shi, Kun Yang, Jing Yang, Cong Shen
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computer Science and Game Theory (cs.GT); Information Theory (cs.IT); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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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 In this paper, researchers investigate the capabilities of pre-trained transformer models in competitive multi-agent games, a subfield known as in-context game-playing (ICGP). They build upon previous work on in-context learning (ICL) in single-agent settings and extend it to decentralized and centralized learning settings for two-player zero-sum games. The authors provide theoretical guarantees that these pre-trained transformers can learn to approximate Nash equilibrium in an in-context manner, leveraging the transformer architecture’s richness to realize celebrated multi-agent game-playing algorithms like decentralized V-learning and centralized VI-ULCB. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how pre-trained models can play games with other smart agents. The researchers show that these models can learn to make good moves without needing a lot of training data. They do this by using the transformer architecture, which is already good at learning from context. In this case, the context is the game being played. The authors also prove that their approach works for different types of games and ways of playing. |
Keywords
* Artificial intelligence * Transformer
