Summary of Can Large Language Models Play Games? a Case Study Of a Self-play Approach, by Hongyi Guo et al.
Can Large Language Models Play Games? A Case Study of A Self-Play Approach
by Hongyi Guo, Zhihan Liu, Yufeng Zhang, Zhaoran Wang
First submitted to arxiv on: 8 Mar 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 introduces a novel approach that combines Large Language Models (LLMs) with Monte-Carlo Tree Search (MCTS) self-play to efficiently resolve deterministic turn-based zero-sum games (DTZG). The authors utilize LLMs as action pruners and proxies for value functions, without requiring additional training. They theoretically prove that the suboptimality of the estimated value in their proposed method scales with ( + + ). Experimental results in chess and go demonstrate the capability of this method to address challenges beyond MCTS and improve LLM performance. The authors’ approach has implications for decision-making aids, particularly in complex scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines two powerful tools: Large Language Models (LLMs) and Monte-Carlo Tree Search (MCTS). It shows how these tools can work together to make better decisions. Right now, LLMs are good at helping with decision-making, but they’re not perfect. They sometimes make mistakes or don’t think things through. MCTS is also good at making decisions, but it has its own limitations. By combining the two, this paper shows how we can get even better results without needing to train the models again. |
