Summary of In-context Exploiter For Extensive-form Games, by Shuxin Li et al.
In-Context Exploiter for Extensive-Form Games
by Shuxin Li, Chang Yang, Youzhi Zhang, Pengdeng Li, Xinrun Wang, Xiao Huang, Hau Chan, Bo An
First submitted to arxiv on: 10 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 tackle a new challenge in game theory by asking whether they can learn a model that can exploit opponents, even those playing according to Nash equilibrium strategies. They introduce the In-Context Exploiter (ICE) method, which trains a single model to adaptively play as any player in the game and exploit opponents through in-context learning. The ICE algorithm involves generating diverse opponent strategies, collecting interactive history training data using reinforcement learning, and training a transformer-based agent within a curriculum learning framework. Experimental results demonstrate the effectiveness of the ICE algorithm in exploiting unknown opponents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new approach to game-solving by developing a model that can exploit any opponent, even those playing according to Nash equilibrium strategies. The In-Context Exploiter (ICE) method trains a single model to adaptively play as any player in the game and learn from interactions with unknown opponents. The researchers show that their algorithm is effective in exploiting these opponents and achieving better results. |
Keywords
* Artificial intelligence * Curriculum learning * Reinforcement learning * Transformer
